Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning-based classification. The employment of deep features through AlexNet architecture with local optimal-oriented pattern can accurately predict skin lesions. The proposed model is tested on two open-access datasets PAD-UFES-20 and MED-NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier. RESEARCH HIGHLIGHTS: The deep features accurately predicted skin lesions through AlexNet architecture with local optimal-oriented pattern. Proposed model is tested on two datasets PAD-UFES-20, MED-NODE comprising melanoma, nevus images and exhibited high accuracy.
Keywords: WHO; classification; handcrafted and deep features; human and disease; medical images; melanoma; nevus; public health.
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