Classification of Skin Cancer Detection using CNN

Using convolutional neural networks (CNN) for skin cancer classification has emerged as a promising method that outperforms traditional methods in terms of accuracy and efficiency. Despite significant advances, the field faces ongoing challenges in ensuring the stability and reliability of these structures. Therefore, a comprehensive review of existing research is urgently needed to address these issues. This review is devoted to an in-depth study of the evolution of CNNs in the classification of skin cancer, showing the progression process and their impact on the accuracy of diagnosis. By adopting a method to select relevant search terms from popular databases, this research aims to better understand CNN-based methods and methods. From our study, we propose to analyze the current state of CNNs in the classification of skin tumors, identifying their strengths and weaknesses while addressing the important issue identified. Our study will reveal the opportunities offered by deep learning in improving diagnostic accuracy, as well as discuss limitations and areas for further research.