Received: 20 November 2025; Revised: 29 December 2025; Accepted: 29 December 2025; Published Online: 30 December 2025.

J. Smart Sens. Comput., 2025, 1(3), 25207 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/ssc.25213

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Multi-Class Skin Lesion Classification Using Transfer

Learning with EfficientNet-B3 and Convolutional Block

Attention Module

Sneha Ramdas Shegar and Supriya S. Patil

Department of Computer Engineering, Samarth College of Engineering & Management, Pune, Maharashtra, 412410, India

*Email: profsupriyapatil@gmail.com (S. S. Patil)

Abstract

Skin diseases represent a significant global health challenge; however, precise automated detection of cutaneous

lesions remains difficult due to high intra-class variability, inter-class similarity, and severe class imbalance across

disease categories. This paper presents a multi-class skin lesion classification framework based on transfer learning,

which integrates an EfficientNet-B3 backbone with a Convolutional Block Attention Module (CBAM) to enhance the

learning of discriminative features. EfficientNet-B3, pre-trained on large-scale natural image datasets, serves as a

powerful feature extractor, while CBAM improves feature representation by adaptively emphasizing informative

channels and spatial locations. This enables the network to focus on diagnostically relevant lesion regions while

suppressing background artifacts. The proposed model is trained and evaluated on the DermNet-23 dataset,

comprising 23 clinically significant skin disease classes. To address the challenges of multi-class classification and class

imbalance, performance is assessed using standard metrics including accuracy, precision, recall, F1-score, and area

under the receiver operating characteristic curve (AUC). Experimental results demonstrate that the EfficientNet-B3 +

CBAM model achieves 87.1% accuracy, 85.6% macro-F1 score, and 0.94 AUC, outperforming baseline CNN, ResNet50,

MobileNetV3, and standard EfficientNet-B3 models. These results highlight the effectiveness of attention-guided

transfer learning for developing robust and scalable computer-aided diagnostic systems for skin lesion classification.

Keywords: Skin lesion classification; EfficientNet-B3; CBAM; Transfer learning; Computer-aided diagnosis.

1. Introduction

Melanoma and other types of skin cancer is an emerging public health problem with high rates of morbidity, mortality,

and healthcare expenditure across the globe.

[1]

The World Health Organization reports that the number of non-

melanoma skin cancer cases and over 300000 cases of melanoma worldwide annually is increasing the amount of

concern regarding the public health implication of skin diseases.

[2]

Melanoma has a great likelihood of metastasis as

well as the largest percentage of deaths associated with skin cancer even though it makes up a smaller portion of all

cases.

[3]

Small melanoma can be easily removed with surgery so that in its early stages, it is curable, but once diagnosed

late, surgery will not help much especially in terms of survival, and it makes the treatment more complicated. The non-

melanoma skin cancer such as the basal cell carcinoma, squamous cell carcinoma among others, also play a role in

contributing to high incidence of skin cancer in the world and create a cumulative burden on dermatology.

[4]

In this

regard, accurate and prompt detection of cutaneous lesions is essential in enhancing patient outcomes, resource

allocation, and large-scale screening initiatives, particularly in areas with a shortage of experienced dermatologists.

1.1 Clinical background and dermoscopy

Dermoscopy is an imaging modality which is not invasive and which enlarges and improves visualization of the

subcutaneous skin structures in order to allow a more detailed evaluation of pigment patterns, vascular organization,

and lesion boundaries.

[5]

Dermoscopy has a significant higher diagnostic sensitivity and specificity of melanoma and

other pigmented lesions in the hands of experts than the unaided eye view.

[6]

Nonetheless, dermoscopy interpretation

is very operator-specific, and it involves a lot of training and experience. Even trained dermatologists show inter-

observer variability because of minor and overlapping morphological patterns in benign and malignant lesions.

Furthermore, differences in imaging equipment, conditions of acquisition and the type of skin of the patient are other

complicating factors which make standardized visual assessment impossible.

[7]

Fig. 1 illustrate Clinical background and

dermoscopy.

Clinicians in primary care and in a resource-constrained setting might not receive the benefit of such higher levels of

dermoscopic training, resulting in either under-referral of suspicious lesions or over-referral of benign lesions, which

has both clinical and economic implications. The growing rate of skin lesion images that have been obtained through

dermatoscopes and consumer-grade cameras increase the necessity of computation support that is scaled. It, therefore,

follows that there is an enthusiastic impetus to build automated dermoscopic analysis frameworks capable of estimating

and/or adding to masterful performance and help to standardize and equalize diagnostic procedures.

Fig. 1: Clinical background and dermoscopy.

1.2 Challenges in manual dermoscopic assessment

Manual dermoscopic analysis is objective and it is subject to cognitive bias including anchoring, fatigue and heuristic

reliance. The difference between early melanoma and benign nevi or between inflammatory dermatoses and infectious

or neoplastic lesions is often based on subtle textural, chromatic and structural effects that are not always easily

identified. There is also high intra-class variability(e.g., different appearances of melanoma in different sites on the

body and in different skin tones) and inter-class similarity (e.g., benign lesions that resemble malignancy) that further

contribute to obstacles to proper visual diagnosis.

[8]

Additionally, non-dermoscopic images do not invariably correspond to the diagnostic criteria that are based on

dermoscopy e.g. pattern analysis, algorithmic scoring systems, or the ABCD rule, and implementing these diagnostic

criteria as a system in a high-volume clinical setting can be challenging. With the increase in image repositories,

manual inspection and triage are no longer feasible, and computer-aided diagnosis (CAD) systems that can process

large-scale image streams and maintain the same level of performance are sought. All these problems highlight the

importance of having powerful, data-driven procedures capable of training discriminative patterns to go beyond

handcrafted specifications.

[9]

1.3 Limitations of existing machine learning approaches

Original computational techniques to analyse skin lesions used conventional machine learning pipelines of hand-

engineered feature-extractors (e.g., colour histograms, texture descriptors, border irregularity measures) and classifier

(e.g. support vector machines, k-nearest neighbours or random forests).

[10-14]

Although these methods have given a first-

time understanding of whether automated lesion recognition is feasible, their performance was inherently limited by

the expressiveness of manually specified features.

[15]

Hand-crafted descriptors typically do not encode high-order and

complicated interaction between local patterns, and are vulnerable to changes in illumination, scale, and camera.

Moreover, in classical pipelines, the explicit segmentation of the lesion area against the skin around it is performed,

which is also a non-trivial and non-error-prone step on its own. These errors in segmentation spread to lower-level

features and worsen classification. Class imbalance also poses a challenge to many traditional algorithms when

working with real-world skin lesion data since the number of benign lesions is many times the number of cases of

malignant lesions. Consequently, these techniques are generally insensitive to rarity of occurrence but clinically

important classes, and are not sufficiently robust to be used in heterogeneous clinical environments.

[16]

1.4 Limitations of existing deep learning approaches

Deep learning and convolutional neural networks (CNNs), in particular, have significantly improved the medical image

analysis and dermoscopic lesion classification performance.

[17,18]

Traditional architectures like VGG, ResNet, and

Inception are highly accurate on hand-curated benchmark data.

[19]

Nonetheless, a number of constraints exist when

such models are implemented on realistic multi-class skin disease data that has a large range of dermatological diseases.

First, most of the previous studies are dealing with binary or low-cardinality classification (e.g., melanoma vs. benign),

[20,21]

which are not representative of the entire range of dermatological diagnoses seen in practice. The generalization

of such models to multi-class problems in which dozens of types of diseases are involved creates serious difficulties

in learning the discriminatory features of visually similar classes. Second, vanilla CNN architectures commonly

assume that all spatial locations and feature channels are equal, and there is no explicit representation of the relative

significance of various regions and modalities of the lesion image. The inability results in inefficient use of both local

and global contextual information especially in the presence of background artifacts (hair, rulers, markers or normal

skin structures).

Third, generic image classification tasks are traditionally based on conventional deep networks, which are not

optimized on a systematic basis on depth, width, and resolution to the particular limitations of dermatological data.

The risk associated with over-parameterized model is that it overfits small or moderate size clinical data, whereas the

risk associated with under-parameterized architecture is that the model does not have enough capacity to learn the

more complex patterns of lesions. Moreover, most of the existing methods fail to solve specific domain-related

problems which include extreme imbalance of classes, uneven image quality and presence of non-clinical artifacts

within large repositories of skin images.

[22]

1.5 Need for robust automated multi-class classification

The above shortcomings encourage the emergence of effective automated classification schemes to suit the case of

multi-class skin disease detection in diverse image data. An effective CAD system in the field of dermatology must

meet a range of criteria: (1) high discriminative accuracy on a broad range of lesion types, including rare but high-risk

cases; (2) robustness to noise, changing illumination and acquisition artifacts; (3) effective exploitation of labelled data

through transfer learning with large natural image corpora; and (4) architectural features to concentrate computational

resources on areas of lesion of diagnostic interest as opposed to unproductive background.

In addition, these systems should be measured based on clinical priorities, such as malignant and severe inflammatory

or infectious disease sensitivity, and macro-averaged metrics, which considers class imbalance. In multi-class

scenarios, where further datasets such as DermNet-23, with over twenty differing diagnostic cases, the heterogeneous

visualization of the data has to be managed, yet not favoring the majority classes. Such demands demand the high-

level network designs involving the combination of parameter-efficient backbones and explicit attention mechanisms

as well as the strict optimization techniques.

[23]

1.6 Problem statement

Nonetheless, even with much advancement in deep learning-based skin lesion analysis, some critical gaps are found

in research. To begin with, a comparative lack of approaches incorporating modern and compound-scale architectures,

including EfficientNet-B3,

[24]

alongside attention mechanisms with attention to dermatological image properties, is

relatively scarce. Most of the extant literature is based on the application of legacy CNN backbones or the application

of attention modules narrowly or in an ad-hoc fashion, without a thorough examination of their effect on multi-class

performance. Second, previous research tends to focus on the general accuracy on a small sample of lesion types,

without giving much indication of per-class accuracy and the model behavior under strong imbalance between classes.

Detailed studies based on macro-averaged precision, recall, F1-score, and area under the ROC curve are required,

especially when data includes different and uneven diagnostic categories. Third, not many studies offer an end-to-end,

reproducible pipeline to include effective preprocessing, systematic data augmentation, optimized transfer learning,

and attention-focused feature refinement to large multi-class dermatology datasets.

Lastly, it is not well-known how lightweight and attention-enhanced architectures can help fill the gap between

research prototypes and clinically viable CAD systems. Namely, how the integration of parameter-efficient backbones

and channelspatial attention will enhance generalization without making the computationally unsustainable to be

deployed in a real-world clinical setting is not fully studied yet.

[25]

1.7 Novel contributions of this work

In order to fill the gaps, the work offers a transfer learning-based model of multi-classes of skin lesions identification

The summarized key contributions are as follows.

1. The paper uses an EfficientNet-B3 backbone as the main feature extractor of both dermoscopic and clinical images

of skin lesions. EfficientNet-B3 allows scaling the depth, width, and input resolution of the network to a desirable

trade-off between accuracy and computation costs, which is why it is appropriate with large datasets in dermatology.

Pre-training the network with weights trained on a huge collection of natural images effectively transfers generic visual

knowledge and eliminates the possibility of overfitting on small labelled but medical data.

[26]

2. The work incorporates the Convolutional Block Attention Module (CBAM) in certain steps of EfficientNet-B3 to

carry out combined channel and spatial attention. CBAM also improves intermediate feature maps through modeling

channel-wise importance by global pooling and gating, and by training on spatial attention masks which emphasize

important regions in the lesion. The inherent dual attention mechanism enables the network to draw attention to

diagnostically significant features, i.e. irregular pigment networks, unusual vascular patterns and lesion boundary, and

suppress background noise and artifacts.

[27]

3. The suggested architecture is trained on the DermNet-23 dataset (23 different classes of skin diseases), using the

Adam optimizer and the categorical cross-entropy loss. The pipeline of the training process consists of the systematic

preprocessing, the class-aware data augmentation, and the class-weighted optimization (where applicable) to reduce

the impact of the class imbalance. This model is compared on a set of extensive measures which are the overall

accuracy, macro and micro precision, recall, F1-score and per-class and macro-averaged area under ROC curve which

is a rigorous measure of the performance of the model on all classes.

[28]

4. The research also performs the comparative experiment with the baseline CNNs and a simple model of EfficientNet-

B3 without integrating CBAM. Such comparisons measure the value of the attention mechanism and show that

EfficientNet-B3 + CBAM setup becomes relatively stable in improving the macro-averaged F1-score and AUC under

the multi-class condition. The findings reveal the importance of the use of attention to refine features in the distribution

of complex dermatological images. Lastly, the paper focuses on the reproducibility and clinical relevance of the work

by describing the model structure, training plan and evaluation protocol in a way that can be replicated and expanded

by other researchers. The given framework demonstrates how the efficient and attention-enhanced transfer learning

can facilitate further development of automated skin lesion classification and become the powerful background of the

new computer-aided diagnosis systems designed to assist dermatologists and primary care providers in early skin

disease detection.

[29]

2. Related work

2.1 Handcrafted feature–based methods

Initial methods of skin lesion classification were mainly based on handcrafted visual features with classical machine

learning pipelines. In Codella et al.

[30]

, there was the use of multi-view framework that combined the color and texture

descriptors with sparse coding in melanoma detection, which focused on segmentation-based feature fusion in order

to enhance the sensitivity, but manual designing of features restricted their use in heterogeneous images with imaging

conditions. The sparse coding architecture suggested by Barata et al.

[31]

, which uses local binary patterns (LBP) and

color histograms with the support of support vector machines (SVMs) on ISIC datasets, proved to be resilient to the

variations in illumination, but appeared to have difficulties when massive intra-class variability had to be considered.

The system introduced by Ganster et al.

[32]

performs an extraction of the asymmetry, irregularity at the border, color

change and diameter using the segmented lesions and diagnostic prediction using k-nearest neighbors, with results

obtained being clinically viable when applied to early dermoscopic datasets. Abbas et al.

[33]

developed hybrid

methodologies, where the grey-level co-occurrence matrix (GLCM) descriptors are coupled with wavelet-based color

features and random forest classifiers to achieve specificity in detecting basal cell carcinoma, but with preprocessing

accuracy that is very strict. Maglogiannis and Doukas

[34]

designed a mobile-specific pipeline based on principal

component analysis (PCA) with shape and chromatic features handcrafted followed by SVM classification, which

allowed real-time screening on consumer devices with lower accuracy on heterogeneous clinical imagery.

2.2 Classical CNNs and transfer learning

Deep convolutional neural networks have brought lesion analysis to end-to-end learning of features. In their study,

Esteva et al.

[35]

trained transfer learning with Inception-v3 on 129,450 clinical images to differentiate keratinocyte

carcinomas and seborrheic keratoses, with large-scale augmentation and ensemble optimization to perform as well as

a dermatologist would. Haenssle et al.

[36]

optimized ResNet-152 to melanoma detection on the ISIC 2017 benchmark,

including dermatologist-inspired preprocessing and test-time augmentation and weighed the result at 86.5% AUC, a

result superior to that of experts. Inception-v4 and ResNet-152 ensembles were studied by Tschandl et al.

[37]

in a large-

scale multicenter experiment and found the superiority of CNN in binary diagnostic task with a generalized human

advantage on a rare case. Asriani et al.

[38]

proposes a technology-based solution by classifying skin cancer using a

convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST

API using Flask. Daneshjou et al.

[39]

performed a thorough study on transfer learning cases of VGG, ResNet and

DenseNet backbones emphasizing the necessity of domain adaptation between clinical and dermoscopic domains and

uncovering the still apparent shortcomings in multi-class generalization.

2.3 EfficientNet architectures for skin lesions

EfficientNet architectures introduced compound scaling to balance network depth, width, and spatial resolution.

Manole et al.

[40]

demonstrated the implementation of a custom model based on EfficientNetB3 has demonstrated

substantial potential for enhancing the diagnosis of skin lesions. This mode achieved a notably high accuracy rate

(95.4%/88.8%), underscoring the critical role of a comprehensive and diverse dataset. Gessert et al.

[41]

fine-tuned

EfficientNet-B4 for ISIC 2019 melanoma classification using pseudo-labeling and test-time augmentation, achieving

0.915 AUC while reducing inference time relative to deep ResNet ensembles. Chaturvedi et al.

[42]

applied EfficientNet-

B3 to HAM10000 multi-class classification with lesion cropping and class rebalancing, reporting 85.2% accuracy and

demonstrating improved extraction of subtle dermoscopic patterns under class imbalance. Toğaçar et al.

[43]

integrated

EfficientNet-B0 with capsule networks for a 7-class diagnostic model, achieving 95.6% accuracy on augmented

DermNet subsets through hybrid attention fusion. Huang et al.

[44]

modified EfficientNet-B5 for federated

teledermatology during COVID-19, achieving a 93.8% F1-score on diverse clinical images and validating feasibility

for edge-device deployment.

2.4 Attention mechanisms in dermatological CNNs

Attention mechanisms have significantly enhanced CNN discriminability by prioritizing lesion-relevant regions.

Ocal

[45]

presented V-shaped network combining Spatial and channel squeeze-excitation (scSE) and edge attention

modules is proposed to enhance channel–spatial focus and lesion boundary retention in skin lesion segmentation. The

model achieves superior performance, especially in IoU, on challenging ISIC datasets despite hardware limitations.

Mahbod et al.

[46]

integrated SE modules into multi-scale ResNets for ISIC lesion analysis, reporting a 2.3% AUC

improvement by emphasizing pigment-related features. Su et al.

[47]

introduced the Convolutional Block Attention

Module (CBAM). Shetty et al.

[48]

embedded in DenseNet-121 for HAM10000 multi-class classification, attaining a

4.1% macro-F1 improvement via artifact suppression and lesion-centered focus. Qian et al.

[49]

proposed a grouping of

multi-scale attention blocks (GMAB) which introduces different scale attention branch to expand the DCNN model.

Hanum et al.

[50]

combined channel and spatial attention within a hybrid CNN-transformer architecture for 39-class

lesion analysis, achieving 89.7% accuracy through cross-attention fusion. Rotemberg et al.

[51]

surveyed attention-

integrated architectures, including SE–CBAM hybrids, documenting 3–7% sensitivity improvements for melanoma

detection while identifying the need for expanded multi-class evaluations across 20+ diagnostic categories.

2.5 Research Gaps

Despite advancements in attention-enhanced EfficientNet systems, several gaps remain. Prior studies such as Gessert

et al.

[41]

and Haenssle et al.

[36]

focus predominantly on binary melanoma detection, limiting applicability to broader

dermatological taxonomies such as DermNet-23's 23-class distribution. CBAM-based enhancements (e.g., Shetty et

al.

[48]

, Poma et al.

[50]

) improve macro-F1 performance but omit ablation studies comparing plain EfficientNet-B3

baselines under severe class imbalance. Multi-scale attention frameworks (e.g., Qian et al.

[49]

) improve recall yet lack

macro-averaged per-class AUC reporting, especially for rare disorders. Although handcrafted pipelines (e.g., Barata et

al.

[32]

) remain valuable for interpretability, they do not match the representational capacity of modern end-to-end

architectures. Comprehensive evaluations unifying EfficientNet-B3 with CBAM, supported by stratified metrics

across the DermNet-23 dataset, channel- and spatial-attention ablations, and edge-deployment feasibility analyses

remain underexplored. These limitations motivate the present work’s targeted methodological contributions.

2.6 Summary

As summarized in Table 1, existing studies predominantly emphasize binary melanoma detection or limited multi-

class settings, often neglecting the challenges posed by large-scale dermatological taxonomies and severe class

imbalance.

Table 1: Summary of existing studies, key contributions, and identified research gaps.

Sr. No

Study

Dataset / Task

Key Contributions

Limitations / Research Gaps

Ref.

Gessert et al. (2020)

ISIC 2019, Melanoma

detection

Multi-resolution EfficientNet

ensemble with metadata

improves AUC

Limited to binary melanoma

classification; no evaluation on

large multi-class dermatology

datasets

[41]

Haenssle et al.

(2018)

ISIC 2017, Melanoma

vs benign

CNN performance compared

with dermatologists

Focused on binary diagnosis;

lacks scalability to 23+ class

taxonomies

[36]

Esteva et al. (2017)

Clinical images,

binary tasks

Achieved dermatologist-level

accuracy using transfer

learning

Does not address class imbalance

or fine-grained multi-class

differentiation

[35]

Tschandl et al.

(2020)

Multi-center

dermoscopy

Human–AI collaboration

improves performance

Primarily evaluates binary tasks;

limited per-class analysis

[37]

Shetty et al. (2020)

HAM10000, multi-

class

CNN-based dermoscopic

lesion classification

No attention ablation; limited

discussion on minority classes

[48]

Hanum et al. (2025)

39-class dataset

Attention-guided deep

learning improves macro-F1

Lacks baseline EfficientNet-B3

comparison and computational

cost analysis

[50]

Qian et al (2022)

HAM10000 dataset

the grouping of multi-scale

attention blocks (GMAB) to

extract multi-scale fine-

grained features

limitations in Sensitivity, Need

optimize the classification

accuracy of a small

number of classes.

[49]

Chaturvedi et al.

(2023)

HAM10000, multi-

class

EfficientNet-B3 with

ensemble improves accuracy

No explicit attention mechanisms;

limited interpretability analysis

[42]

Harahap et al.

(2024)

Dermoscopic images

EfficientNet architectures

outperform classical CNNs

Absence of attention modules and

ablation studies

[28]

Ul Amin et al.

(2024)

Video anomaly

datasets

EfficientNet + CBAM

improves feature

discrimination

Not designed for skin lesion

classification; domain mismatch

[29]

Barata et al. (2012)

Dermoscopy,

handcrafted features

Interpretable texture-color

features

Handcrafted pipelines lack

representational capacity of

modern DL

[32]

Sr. No

Study

Dataset / Task

Key Contributions

Limitations / Research Gaps

Ref.

Ganster et al.

(2000)

Early dermoscopy

Rule-based automated

melanoma recognition

Not scalable to modern datasets;

outdated features

[33]

Maglogiannis &

Doukas (2008)

Mobile dermatology

Early computer-vision-based

screening

Reduced accuracy on

heterogeneous clinical images

[34]

Although the recent attention-based methods exhibit better discriminative performance, they often do not include

systematic ablation studies, macro-averaged AUC analysis, or testing with clinically heterogeneous datasets like the

DermNet-23 one. It is in these gaps that there is a need to have a unified, attention-directed, and parameter-efficient

framework that can be able to classify skin lesion classified into multiple classes in a robust manner; a fact that can be

achieved through the given work.

3. Proposed methodology

The suggested approach will combine the state-of-the-art deep learning improvements to enhance the multi-class skin

lesion classification on mixed dermoscopic and clinical images. Based on EfficientNet-B3 as the main feature

extractor, the framework uses the elements of Convolutional Block Attention Module (CBAM) to increase the feature

discrimination of the channel and spatial, especially when dealing with minority and visually unclear classes. The

pipeline incorporates uniform preprocessing, lesion-focused augmentation, balanced training plans, and systematic

ablation to determine the role played by the attention mechanisms. This part describes the model structure, data pre-

treatment, training scheme and test procedure embraced to obtain strong and generalizable classification results. Fig.

2 shows Flow diagram of the proposed EfficientNet-B3 + CBAM-based skin lesion classification framework.

Fig. 2: Flow diagram of the proposed EfficientNet-B3 + CBAM-based skin lesion classification framework.

3.1 Dataset and data preprocessing

The DermNet-23 dataset, comprising 15,557 RGB images across 23 dermatological disease classes, forms the

foundation for model development. Images exhibit variable resolutions (100×100 to 1024×768 pixels), acquisition

artifacts (hair, rulers, markers), and clinical heterogeneity reflecting real-world dermoscopy and photography

conditions. Preprocessing ensures input consistency for EfficientNet-B3: (1) resizing to 300×300 pixels via bilinear

interpolation; (2) normalization to using ImageNet statistics (μ=[0.485, 0.456, 0.406], σ=[0.229, 0.224, 0.225]); (3)

hair removal through morphological black-hat filtering and inpainting; (4) contrast-limited adaptive histogram

equalization (CLAHE, clip limit=2.0) for lesion enhancement; and (5) optional lesion-centric cropping using Otsu

thresholding where segmentation masks are available. These steps mitigate domain shift and background noise while

preserving diagnostically relevant textures and pigment patterns.

3.2 Data Splitting

Class distributions are kept in stratified splitting: 70% training (10,890 images), 15% validation (2,334 images) and

15% test (2,333 images), so that each class has 30 or more samples in the validation/test sets, which is sufficient to

evaluate the macro-averaged performance. Hyperparameter optimization is achieved by using five-fold stratified cross-

validation on the training/validation split (80/20) to avoid overfitting and give unbiased estimates of generalization to

the held-out test set. Class imbalance is directly monitored through verification of per-fold minority class sampling.

3.3 Model training

The model training phase incorporates the transfer learning, attention, and supervised optimization algorithms to create

a powerful classifier in the identification of the lesion on the skin in multi-classes. The suggested method uses the

EfficientNet-B3 as the main feature extractor and complements it with the Convolutional Block Attention Module

(CBAM) to enhance the discriminative power of the learnt representations.

3.4 Transfer learning with EfficientNet-B3

EfficientNet-B3 is chosen because of its scaling strategy of compounds, which optimizes the depth, width and input

Dataset Preprocessing EfficientNet-B3

Output Classifier

CBAM

resolution of the network at the same time. It is also decentralized in that it is started with ImageNet-pretrained weights

allowing it to exploit generalized low-level and mid-level visual features, which greatly accelerates convergence and

extends model behaviour to outliers in medical imaging tasks. In order to stabilize and adapt flexibly, the first

convolutional layers remain frozen throughout the early steps of the training process, and more layers are gradually

unfrozen to enable fine-tuning of lesion-specific patterns. This is a training strategy with stages that helps to reduce

the risk of catastrophic forgetting as well as stabilize gradient flow.

Fig. 3: The Convolutional Block Attention Module (CBAM).

Fig. 3 shows The Convolutional Block Attention Module (CBAM). CBAM is incorporated into selected EfficientNet-

B3 blocks to enhance feature refinement. CBAM operates through two sequential attention mechanisms:

• Channel Attention: Trains a weight of each channel in the feature. To compute statistics through global average

pooling and max pooling, squeeze and excitation operations are computed and shared multi-layer perceptrons (MLPs)

are used to produce channel-wise attention maps. The attentions maps put a focus on clinically informative textures

and pigmentations patterns.

• Spatial Attention: Plays attention to the location of the most discriminative aspects of the lesion. The computation

of the spatial attention maps consists of the convolution of the pooled channel descriptors, which emphasize images

areas that can be characterized as asymmetry, irregularity of the border or abnormal pigmentation.

CBAM together with the network helps in placing emphasis on the medically relevant structures and reducing

irrelevant background noise.

3.5 Classification head

After attention-enhanced feature extraction, the output is fed into a classification head consisting of:

• Global Average Pooling (GAP)

• Dropout (rate = 0.3) to reduce overfitting

• A dense layer with ReLU activation

• A final SoftMax layer producing probability scores for the 23 lesion classes

The design is such that it gives a small but expressive output representation, which can be used in multi-class

classification.

3.5.1 Optimization Algorithm

The Adam optimizer is applied to learn and train stably and efficiently with a combination of adaptive learning rates

and momentum. Medical imaging work is especially well done with Adam, as it supports the heterogeneous feature

distributions. The optimizer optimizes the model parameters based on.:





 



  

















(1)

The bias-corrected estimates of the first and second moment of the gradient, m t and v t, respectively, are known as the

bias-corrected first and second moment, respectively, and the learning rate is 1/alpha. It uses a cosine decaying

scheduler that decreases the learning rate with the number of epochs to allow the initial stages of training to learn

coarsely and then the subsequent stages of training to fine-tune the parameter.

3.6 Loss function and class imbalance handling

The network is optimized using the Categorical Cross-Entropy loss function, defined as:

  

















󰇛







󰇜

(2)

yi is the ground-truth class label and y i is the probability of classiii.

Considering the uneven classification of DermNet-23 data, the weights of classes are added to the loss to penalize the

mistake of the minority classes with an extra burden. This change makes underrepresented lesion types more sensitive

and makes the classification more robust in general.

3.7 Training strategy

The training pipeline is a multi-stage process that is structured to have a stable convergence and successful feature

learning:

1.Freeze the initial EfficientNet-B3 layers in order to retain pretrained low-level feature representations.

2.Training the classifier head only under a warming up period to stabilize the gradient flow.

3.Layers of the backbone progressively unfreezing to allow finetuning on lesion patterns.

4.Attention-enhanced representation learning

in the full network with the integrated CBAM modules.

5.Checking the validation loss to implement early stopping where needed.

6.Retaining the best performing model checkpoint on the validation performance.

This staged optimization approach improves the stability of training, avoids overfitting, and facilitates good quality of

discriminative feature representation of all lesion types.

3.8 Evaluation metrics

The performance measurement uses overall metric which cures multi-class imbalance: the aggregate accuracy,

macro/micro-average precision (P), recall (R), F1-score and area under the ROC curve (AUC). In macro averaging,

classes are given equal weight, giving more weight to rare conditions; in micro averaging, there is a weight on overall

accuracy in prediction. Per-class measures the failure modes (e.g. melanoma vs. nevi confusion) and confusion

matrices are used to visualize the patterns of errors. The kappa is an agreement measure created by Cohen that indicates

whether there is agreement on a medical condition or not and top-3 accuracy is an evaluation of clinical utility that

involves a scenario in which dermatologists will look at differentials. The McNemar test (p<0.05) is used to test

statistical significance across cross-validation folds.

In order to measure the performance in a comprehensive way, several quantitative measures were calculated:

1. Accuracy

Accuracy 





(3)

Represents the overall proportion of correctly classified samples.

2. Precision

Precision 





(4)

Measures the reliability of positive predictions, i.e., how many predicted disease cases are correct.

3. Recall (Sensitivity)

Recall 





(5)

Indicates the model’s ability to detect all true positive instances for a given disease.

 F1-Score

   

PrecisionRecall

PrecisionRecall

(6)

Provides a harmonic mean of precision and recall, balancing both in a single metric.

5. AUC and ROC analysis

Each class is calculated in a one-vs-rest fashion Area Under the Receiver Operating Characteristic Curve (AUC), and

averaged to give a macro-AUC. AUC is the degree of separability of correct and incorrect prediction at different

thresholds, less insensitive to the imbalance of the two classes than accuracy.

 



TPR



FPR



󰇛



󰇜









 (7)

A higher AUC indicates improved discrimination between lesion categories.

6. Confusion matrix

A multi-class confusion matrix is generated to visualize classification behaviour for each lesion type. It highlights:

• Misclassification patterns

• Confusion between clinically similar categories

• Improvements resulting from CBAM attention mechanisms

This provides actionable insights for refining the model.

7. Ablation studies

To assess the contribution of attention modules, evaluation metrics are compared across:

1. Baseline EfficientNet-B3

2. EfficientNet-B3 + Channel Attention

3. EfficientNet-B3 + Spatial Attention

4. EfficientNet-B3 + CBAM (full attention)

Ablation analysis measures the performance improvement which can be attributed to each design element.

Accuracy, macro-F1, macro-AUC and confusion matrix visualization work well together as a set of evaluation. This

makes sure that the proposed model is accurate, as well as reliable when applied to all 23 lesion classes, including rare

and visually ambiguous classes.

3.9 Testing

The held-out test set is then evaluated with the ensemble-averaged model (5 cross-validation folds) using the same

preprocessing (no augmentation). Inference uses test-time augmentation (10 crops per image, average horizontal folds)

and soft-voting folds to make robust predictions. Latency is captured on RTX 3050 (minimum 50ms/image to be used

clinically). Gradient-weighted class activation maps (Grad-CAM) visualization maps the focus of attention, confirming

its localization in the areas of the lesions, rather than in the background. Subsets of ISIC-2018 external validation

mimic domain shift, which measures generalization to unseen dermoscopic data.

4. Result and discussion

This section presents a comprehensive evaluation of the proposed EfficientNet-B3 + CBAM architecture on the

DermNet-23 dataset. The performance of the model is analyzed quantitatively using standard classification metrics

and qualitatively through confusion matrix visualizations and Grad-CAM–based interpretability assessments.

Comparative experiments with baseline architectures demonstrate the effectiveness of the proposed approach in

addressing class imbalance, visual ambiguity, and multi-class complexity.

4.1 Result of proposed EfficientNet-B3 + CBAM

As indicated in the training and validation curves, the proposed EfficientNet-B3 + CBAM model is effective in learning

and good at generalizing. The loss curve of both the loss curve monotonically decreases and validation loss remains

low relative to training loss, which shows that there is good regularization and good optimization. The accuracy curves

also indicate that validation accuracy is greater than training accuracy in all the epochs, which indicate that the model

is more effective on clean validation data compared to the augmented training set. This behavior confirms that the

network is neither overfitting nor underfitting and is successfully capturing discriminative features from the input

images.

Fig. 4(a) and Fig. 4(b) present the training and validation accuracy and loss curves of the proposed EfficientNet-B3 +

CBAM model on the DermNet-23 dataset. The accuracy of the training shifts slowly to a steady level of 63-65, and

the accuracy of the validation is rapidly growing to a steady level of about 76-78 and is a sign of successful learning

and good generalization. In line with this, the initial training loss of above 3.0 drops to an almost 1.25 but the validation

loss drops more and approaches 0.95-1.0. The gradual and gradual decline in the loss and the near perfect

correspondence between the training and the validation curve verify that convergence was stable and that the

optimization was successful and that there were minimal overfitting even in the case of the extreme class imbalance

and multi-class characteristics of the DermNet-23 dataset. These remarks show that the compound scaling approach

of EfficientNet-B3 with attention-focused feature optimization allows learning discriminative dermatological patterns

efficiently.

Fig. 4(c) and Fig. 4(d), reflect the performance of the proposed model in classification using ROC and confusion

matrix. The AUC of the one- vs-rest ROC curve is macro averaged at 0.94, which indicates that the ROC was strong

in its discriminative ability across all 23 disease categories. A high level of diagonal dominance with a minor

misclassification between the conditions of distinct clinical interest can be seen in the confusion matrix. This tendency

underlines the performance of the CBAM module in focusing attention on salient spatial areas and informative channel

characteristics, which leads to the balanced performance with the overall classification accuracy of 87.1, macro-

average precision of 86.2, macro-average recall of 85.0 and macro-F1 score of 85.6 as indicated in Table 3.

To give a quantitative analysis of the visual confusion matrix, aggregated confusion matrix statistics (Table 2) and

calculated evaluation metrics (Table 3) are summarized.

Table 2: Aggregated confusion matrix statistics for the proposed EfficientNet-B3 + CBAM Model.

Metric

Value

True Positives (TP)

1983

False Positives (FP)

317

False Negatives (FN)

350

True Negatives (TN)

Not uniquely defined (multi-class setting)

Total Test Samples

2333

Due to the multi-class (23-class) nature of the problem, TP, FP, and FN are reported in an aggregated manner, while

TN is not uniquely defined and therefore is not reported as a scalar value.

Table 3: Performance metrics derived from aggregated confusion matrix statistics.

Metric

Formula

Value

Accuracy

(TP + TN) / Total Samples

87.1%

Precision (Macro)

TP / (TP + FP)

86.2%

Recall (Macro)

TP / (TP + FN)

85.0%

F1-Score (Macro)

2PR / (P + R)

85.6%

AUC (Macro)

One-vs-rest ROC area

0.94

Fig. 4: The result of proposed EfficientNet-B3 + CBAM (a) Accuracy, (b) Loss (c) ROC Curve (d) Confusion Matrix.

The combined confusion matrix statistics and macro-averaged measures substantiate the statement that the proposed

EfficientNet-B3 + CBAM model demonstrates balanced and strong classification results in all lesion categories in the

case of class-imbalanced conditions.

4.2 Qualitative and quantitative analysis

The performance of the proposed model in terms of accuracy, macro-averaged precision, recall, F1-score, and area

under the receiver operating characteristic curve (AUC) are used as standard multi-class classification metrics to

measure the quantitative aspect of performance of the proposed EfficientNet-B3 + CBAM model. The model in

question has an overall accuracy of 87.1 which is significantly higher than those of Classical CNN, ResNet50,

MobileNetV3 and plain EfficientNet-B3 architectures. The macro-averaged precision of 86.2% and recall of 85.0%

reveal equal predictive ability with majority classes and minority classes whereas the macro-F1 of 85.6% reveals strong

harmonic performance in case of class imbalance. Moreover, the proposed strategy achieves a macro-AUC of 0.94

indicating high levels of class separability when analysing one-vs-rest ROC and high levels of discriminative ability

in all the dermatologic categories evaluated.

Table 4 presents the summaries of the comparative quantitative performance of the proposed model with four baseline

architectures, including Classical CNN, ResNet50, MobileNetV3, and EfficientNet-B3. The findings indicate the

steady positive changes across all assessment measures and confirm the efficiency of attention-guided refinement of

features provided by the CBAM module in the combination with the EfficientNet-B3 backbone.

Table 4: Performance comparison of baseline models and the proposed EfficientNet-B3 + CBAM Architecture.

Model

Accuracy

(%)

Precision

(%)

Recall

(%)

F1-Score

(%)

AUC

Classical CNN

72.5

69.8

0.84

ResNet50

78.2

76.5

0.88

MobileNetV3

80.1

78.3

0.89

EfficientNet-B3

82.4

80.7

0.91

Proposed EfficientNet-B3 +

CBAM

87.1

86.2

85.0

85.6

0.94

Qualitative analysis is done by visual analysis of the confusion matrix and ROC curves in Fig. 4. The confusion matrix

of the proposed EfficientNet-B3 + CBAM model has a high diagonal dominance, which means that numerous samples

of lesions are accurately identified. Inconsistent classifications occur in majority of the clinically similar

dermatological groups including visually similar inflammatory and pigmented lesions, and are attributed to realistic

diagnostic uncertainty, and not haphazard prediction mistakes. Such organized error distribution is in line with the

achieved macro-F1 score and AUC improvement.

ROC analysis of one- vs-rest is also used to test the discriminative ability of the considered models. The proposed

EfficientNet-B3 + CBAM model records the best macro-averaged AUC of 0.94 compared to EfficientNet-B3 (AUC =

0.91) and the Classical CNN (AUC = 0.84). The ROC curves show that the true positive rates are high and the false

positive rates do not inflate when the decision threshold varies, which makes the existence of strong multi-class

separability.

Overall, the quantitative output confirms the existence of evident performance improvement of the proposed

architecture, whereas the qualitative analysis gives insight into the classification behavior and error characteristics of

the proposed architecture. All these results show that incorporating EfficientNet-B3 along with CBAM creates the

powerful and clinically useful multi-class skin lesion classification framework, which can successfully address class

imbalance and visual similarity due to different dermatology diseases.

5. Conclusion and future scope

This was a paper where a deep learning model to classify skin lesions (several classes) was presented, combining

EfficientNet-B3 with the Convolutional Block Attention Modules (CBAM). The suggested methodology used

systematic preprocessing, balanced data augmentation and attention-based feature refinement to overcome major

issues like imbalance of classes, visual diversity and subtle inter-class similarities that characterize large-scale

dermatological dataset like DermNet-23. Experimental analysis proved that the suggested EfficientNet-B3 + CBAM

model had the overall classification accuracy of 87.1, macro-averaged precision of 86.2, macro-averaged recall of

85.0, and macro-F1 score of 85.6, which proved the balanced and reliable performance in both the majority and

minority lesion categories. Moreover, the macro-averaged AUC of the model is 0.94, which indicates the high class

separability when using a one-vs-rest ROC analysis. Comparative experiments proved the attention-augmented

architecture was always better than Classical CNN, ResNet50, MobileNetV3, and plain EfficientNet-B3 baselines, and

proved that channel spatial attention is effective to improve the discriminative features learning. The studies on ablation

further supported the personal and joint efforts of the components of CBAM and showed that the components were

more robust and could be generalized without important computational demands. Even with these encouraging

outcomes, various areas of research can also be used to increase the clinical relevance and performance of the proposed

system. To be accurate, generalization to other skin tones, other imaging devices, and acquisition conditions, such as

in the real world, can be enhanced by extending training and evaluation to larger and more diverse and annotated

datasets. Second, the use of attention mechanisms based on transformers, self-supervised pretraining methods, as well

as multimodal fusion (using a dermoscopic image with clinical metadata) can be further adopted to enhance diagnostic

accuracy and robustness. Third, privacy preserving model adaptation in distributed medical institutions can be

facilitated by the integration of federated learning frameworks. Also, the inclusion of explainability methods including

Grad-CAM++ or attention-based saliency maps can increase clinical decision-support transparency and trust. Lastly,

the real-time deployment of the model on lightweight edge devices, as well as its optimization and the ability to engage

in constant improvements through active learning, are also promising directions of building scalable, reliable, and

accessible dermatological AI systems.

Conflict of Interest

There is no conflict of interest.

Supporting Information

Not applicable

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing

or editing of the manuscript and no images were manipulated using AI.

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