Analysis of computer aided automatic skin lesion diagnosis and classification using deep learning models

Abstract

Melanoma is a form of skin cancer that can easily spread to other organs. Therefore, it is necessary to identify melanoma at the beginning level. Visual tests at the time of medical examination of skin lesion are challenging process as there exists high resemblance among the lesions. In addition, dermoscopy is a non-invasive imaging tool that uses a light magnifying device and immersion fluid to allow for the vision of the skin s surface. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. The healthcare industry has benefited greatly from the recent developments in Machine Learning (ML), especially deep learning (DL). Recently, occurrence of skin cancer is considerably noticed among people globally. Earlier detection of skin cancer can result in reduced death rate. Dermoscopy is an effective way to detect and classify skin cancer. Since the visual examination of dermoscopic images is a tedious and cumbersome process, automated tools using Computer Aided Diagnosis (CAD) model becomes essential. newline