Intelligent Skin Cancer Detection with Preliminary Diagnosis using CNN

Abstract

Abstract: This paper addresses the critical need for early and accurate diagnosis of skin cancer, a prevalent global health concern. Recognizing the challenges posed by prolonged waiting times and subjective evaluations in clinical set things, the study focuses on leveraging deep learning techniques to enhance skin disease classification and detection. The research confronts the inherent class imbalance issue, where the number of affected classes is notably lower than the healthy class and strives to elucidate the decision-making processes employed by the models. The authors suggest a comprehensive smart healthcare system implemented through an Android application from start to finish. Evaluating the effectiveness of the proposed deep learning technique, the study utilizes ResNet50, DenseNet169, VGG16, Xception, and DenseNet201 for classification, with Xception achieving a notable 96% accuracy. Additionally, YoloV5, YoloV6, YoloV7, and YoloV8 models are employed for skin lesion detection. Notably, ResNet50 attains a commendable 90% training accuracy, while Xception demonstrates potential for further performance enhancement. This comprehensive exploration of diverse models and techniques contributes to advancing skin cancer diagnosis, emphasizing the importance of accuracy in patient outcomes.