Unveiling Skin Cancer: A Review of Deep Learning Techniques for Detection

Abstract

Early identification is essential for successful treatment and better patient outcomes since skin cancer is caused by damage to DNA in skin cells, which can result in genetic abnormalities and the possibility of metastasis. The present systematic review scrutinizes the efficacy and obstacles of early detection techniques, which differentiate between benign and malignant lesions by examining lesion attributes such as symmetry, color, size, and form. Image quality and expert interpretation are two elements that have an impact on the accuracy of existing techniques. This review assesses the accuracy, precision, and other validation measures of a variety of computer vision approaches and algorithms, including machine learning and deep learning. The results show how these sophisticated algorithms have significantly improved cancer identification, laying the groundwork for further studies and highlighting prospects to improve deep learning-based skin cancer detection.