Novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Abstract

Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.