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Skin Cancer Is One Of The Most Prevalent Forms Of Cancer Worldwide, And Its Early Detection Is Crucial For Effective Treatment And Improved Survival Rates. Traditional Diagnostic Methods Rely Heavily On Clinical Expertise And Invasive Biopsy Procedures, Which Are Often Time-consuming, Costly, And Subject To Human Error. With Advancements In Artificial Intelligence, Image Processing Combined With Machine Learning Has Emerged As A Powerful Tool For Automated Skin Cancer Classification. This Study Focuses On Developing A System That Leverages Image Preprocessing Techniques—such As Noise Removal, Contrast Enhancement, And Segmentation—to Accurately Extract Relevant Features From Dermoscopic Images. Machine Learning Algorithms Are Then Applied To Classify Skin Lesions Into Benign Or Malignant Categories. The Proposed Approach Aims To Improve Diagnostic Accuracy, Reduce Dependency On Manual Interpretation, And Provide A Cost-effective, Non-invasive Solution For Early Skin Cancer Detection. By Integrating Image Processing And Intelligent Classification Models, This Work Contributes To The Advancement Of Computer-aided Diagnosis In Dermatology And Supports Timely Clinical Decision-making.