An attention based optimized network for the classification of skin lesions

A skin lesion, in medical terminology, refers to any abnormality on the skin's surface. Skin lesions, both benign and malignant, can progress to skin cancer, highlighting the need of early identification. The scarcity of dermatological resources, particularly in rural regions, contributes to delayed diagnosis. Existing diagnostic techniques, which heavily rely on the expertise of dermatologists, are plagued by disparities in global healthcare access. Skin lesion classification plays a vital role in early intervention and timely treatment of various dermatological conditions. In this paper, a novel and optimal method to classify different type of skin lesions is proposed, by combining deep learning with optimization techniques. The proposedalgorithm uses the robust RegNetY032 model with a modified classification head as the backbone architecture to perform feature extraction and classification from the dermoscopy images, integrated with the Soft Attention Block to effectively discern and prioritize salient lesion features while disregarding artifacts such as hair and veins commonly present on the skin. Furthermore, Harris-Hawks Optimization is employed to perform hyperparame- ter optimization, enhancing the model's classification performance. Experimental results on the HAM10000 benchmark dataset demonstrate the robustness of the proposed model, achieving superior classification accuracy of 99.27%. The proposed HHO-Reg-SA-Net holds promise for advancing automated dermatological diagnosis systems, contributing to an improved patient care and early intervention in skin cancer diagnosis and treatment.