A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN)

Abstract

Skin cancer is a significant health risk that requires early detection for effective treatment. This paper discusses two automated techniques, Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), which make use of deep learning techniques for skin cancer detection. Through evaluation of research on skin cancer detection using ANN and CNN, the effectiveness and performance of these techniques in early and efficient diagnosis of skin cancer were established. The study found that ANN and CNN were successful in early detection of skin cancer using different data sets and hybrid models, demonstrating the potential for these technologies to improve accuracy in skin cancer detection. The paper highlights the novelty of using deep learning techniques for skin cancer detection and emphasises the critical need for an automated system for skin lesion recognition to reduce effort and time in the diagnosis process. The possible applications of this study include the development of more efficient and accurate skin cancer detection systems that can lead to earlier diagnosis and improved treatment outcomes. Overall, this research underscores the importance of using advanced technologies, such as ANN and CNN, in the fight against skin cancer and highlights the potential impact of these techniques in improving patient outcomes.