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.

Graphical Abstract

Novelty Statement

This study introduces a transfer learning framework for multi-class skin lesion classification that integrates an EfficientNet-B3 feature extractor with Convolutional Block Attention Modules (CBAM) to improve channel–spatial feature refinement.