<![CDATA[Early detection of melanoma is essential to reduce skin cancer related mortality. This study proposes a machine learning based framework for automatic classification of dermoscopic skin lesion images into melanoma and benign categories. The methodology integrates image preprocessing, deep convolutional feature extraction, and an attention mechanism to enhance clinically relevant patterns such as asymmetry, border irregularity, and color variation. The model was trained and evaluated using a labeled dermoscopy dataset with a structured train validation test split. Experimental results demonstrate strong diagnostic performance, achieving 92.8% accuracy, 94.1% sensitivity, 91.3% specificity, and an AUC of 0.96. The high sensitivity indicates effective identification of malignant cases, which is critical for early intervention. Overall, the proposed framework shows promising potential as a computer aided diagnostic tool to support dermatologists in improving consistency, efficiency, and reliability in melanoma detection.]]>
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