A Review on the Various Methods for Classifying Skin Cancer

Skin cancer, a pervasive threat to global health, prompts a critical need for enhanced diagnostic accuracy Conventional methods relying on image segmentation struggle with the intricate diversity in sizes and shapes of skin lesions Our study introduces an innovative automated system that integrates diverse techniques, strategically selecting optimal features to refine diagnostic precision for skin cancer. Utilizing four pre-trained transfer learning Convolutional Neura Network (CNN) models-DenseNet121, ResNet50, VGG16, and ResNet18-the framework rigorously evaluates classification accuracy with a comprehensive skin dataset. Further refining accuracy, the study incorporates preprocessing steps and explores simulation parameters such as epochs, batch size, and optimizers. Meticulous analyses reveal that the most favorable accuracy outcomes are achieved with the proposed ResNet50 and ResNet18 models, particularly with a batch size of 32 coupled with the SGD optimizer. Beyond technical findings, this research marks a profound shift toward the automation of diagnostic processes, striving for error-free outcomes and emphasizing the transformative potential of deep learning models in dermatology. The integration of these models not only enhances diagnostic efficiency but also underscores potentia cost-effectiveness in healthcare, contributing to a broader discourse on leveraging technology to reshape medical diagnostics and setting a precedent for future research endeavors aimed at revolutionizing healthcare practices.