Convolutional Neural Network based Bi-Diverse Activation for Skin Lesion Classification

Abstract

Melanoma is the most genuine sort of skin malignancy. It is usually caused by high exposure to ultraviolet radiation and damages the skin, and forms pigmented melanin. Early identification of melanoma skin infection can expand the endurance rate. A dermatologist essentially follows a succession of steps for skin lesion appraisal, beginning with independent eye screening of skin lesions, at that point by dermoscopy and next by biopsy. Convolutional neural network manifest potential for better classification of skin lesions. Clinical assessment in dermoscopy images is a tedious errand to the dermatologists because of ancient rarities nearness. In this paper, the bi-diverse activation convolutional neural network model has been developed to classify the skin lesion as benign and malignant by applying different activation functions of the ReLU and Swish process. The proposed approach is tried on the ISIC dataset having an all outnumber of 2637 training parts, and 660 images of testing parts categorize into melanoma and benign skin lesion images. Our proposed model accomplishes empowering results having 84% exactness