Cutaneous and ocular toxicology · 2026
Deep learning-based multi-class classification of cutaneous lesions for dermatological assessment.
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Ravisankar S, Karthikeyan M
Аннотация
BACKGROUND: Skin cancer is one of the most common malignancies worldwide, and early detection is essential for improving treatment outcomes and reducing mortality. Conventional diagnostic approaches rely on visual examination and dermoscopic analysis by dermatologists, which can be time-consuming and subject to inter-observer variability. Recent advances in artificial intelligence have enabled the development of computer-aided diagnostic systems to support clinical decision-making in dermatological oncology.
OBJECTIVE: In this study, a deep learning-based framework is proposed for multi-class classification of cutaneous lesions using dermoscopic images.
METHODS: The proposed model integrates a Vision Transformer (ViT) to capture global contextual features and a Squeeze-and-Excitation Residual Network (SE-ResNet) to extract channel-refined local features. These complementary representations are combined using an adaptive attention-based fusion mechanism to improve classification performance. In addition, an Improved Crocodile Optimization Algorithm (ICOA) is employed to optimize model hyperparameters and enhance convergence stability.
RESULTS: The proposed method was evaluated using the HAM10000, ISIC 2019, and PH2 datasets, achieving classification accuracies of 99.05%, 98.31%, and 99.17%, respectively.
CONCLUSION: The results demonstrate the robustness and generalizability of the proposed framework, highlighting its potential as a clinical decision-support tool for early detection and improved diagnosis of skin cancer.
OBJECTIVE: In this study, a deep learning-based framework is proposed for multi-class classification of cutaneous lesions using dermoscopic images.
METHODS: The proposed model integrates a Vision Transformer (ViT) to capture global contextual features and a Squeeze-and-Excitation Residual Network (SE-ResNet) to extract channel-refined local features. These complementary representations are combined using an adaptive attention-based fusion mechanism to improve classification performance. In addition, an Improved Crocodile Optimization Algorithm (ICOA) is employed to optimize model hyperparameters and enhance convergence stability.
RESULTS: The proposed method was evaluated using the HAM10000, ISIC 2019, and PH2 datasets, achieving classification accuracies of 99.05%, 98.31%, and 99.17%, respectively.
CONCLUSION: The results demonstrate the robustness and generalizability of the proposed framework, highlighting its potential as a clinical decision-support tool for early detection and improved diagnosis of skin cancer.