AI Derm: Empowering Skin Cancer Diagnosis With Deep Learning On Mobile Devices

Abstract

One of the worst forms of cancer is skin cancer, which is caused by damaged DNA in skin cells that eventually results in genetic abnormalities and malignancy. Because of its increasing frequency, high death rate, and rising healthcare expenses for treatment, timely identification becomes critical. As a result, several early screening techniques have been developed, mostly using symmetry, color, size, and shape of the lesion to distinguish benign from malignant forms, especially melanoma. An extensive analysis of deep learning methods for early skin cancer diagnosis is presented in this study. After a careful examination of relevant research articles that have been published in reputable journals, thorough assessments are presented in formats that range from tools, graphs, and tables to techniques and frameworks, guaranteeing comprehension and accessibility. .. Traditionally, dermoscopes have been used by dermatologists or primary care physicians to visually screen patients and diagnose skin conditions. In order to confirm the diagnosis and customize therapy, patients who exhibit early indications of skin cancer undergo biopsy and histological analysis. Notably, deep convolution neural networks (CNNs) have advanced to the point that they can now automatically classify skin cancer on par with dermatologists. But as this discussion explains, a widely accepted and clinically sound approach to the diagnosis of skin cancer still eludes us.