Skin Cancer Classification using Multiple Convolutional Neural Networks

Abstract

Skin cancer is a significant threat to the global health, with over 2.1 million new cases diagnosed annually worldwide. Timely detection and treatment are vital for improving survival rates, yet the limited availability of dermatologists in remote regions poses a significant barrier. The utilization of Artificial Intelligence (AI) and Deep Learning (DL) has seen a remarkable surge in recent years for skin cancer prediction. This study conducts an in-depth review of advanced skin cancer prediction methods employing deep learning techniques and explores the diverse array of machine learning algorithms applied in this context. Skin cancer comprises seven distinct diagnoses, presenting a formidable challenge for dermatologists due to the overlapping phenotypic traits. Conventional diagnostic accuracy typically ranges from 62% to 80%, underscoring the potential of machine learning to enhance diagnosis and treatment. While some researchers have created binary skin cancer classification models, extending this to multiple classes with superior performance has been elusive. A deep learning classification model for various skin cancer types, yielding promising results that highlight the superiority of deep learning in classification tasks is developed. The experimental outcomes demonstrate that the individual accuracy of Sequential, ResNet50, DenseNet201, VGG-16 and EfficientNetB0 models are aggregated and yields the maximum occurring output value from all the models. Furthermore, a comparative analysis with the latest skin classification models underscores the superior performance of the proposed multi-type skin cancer classification model.