Comparative Analysis of Machine Learning Models for Automated Skin Cancer Detection: Advancements in Diagnostic Accuracy and AI Integration

Skin cancer detection remains a critical challenge in dermatology, with early diagnosis significantly improving patient outcomes. This study presents a comparative analysis of machine learning models for automated skin cancer detection, highlighting the superior performance of Convolutional Neural Networks (CNNs). The CNN model achieved the highest accuracy (92.5%), sensitivity (91.8%), and specificity (93.1%) compared to other algorithms such as Support Vector Machines (SVMs) and Random Forests. The use of advanced preprocessing techniques and diverse datasets ensured the model's robustness and generalizability. While the findings demonstrate the potential of deep learning in dermatological diagnostics, limitations such as model interpretability and dataset diversity were identified. This research underscores the transformative role of AI in improving diagnostic accuracy, enabling early detection, and addressing healthcare disparities, particularly in resource-constrained settings. Future work aims to enhance model explainability and expand its applicability across diverse populations.