Automated Melanoma Recognition in Dermoscopy Images Via Deep Residual Networks

Melanoma, the most dangerous of skin cancers, needs early and correct diagnosis togreatly enhance patient survival. This paper proposes a deep learning-based framework for the computer-aided detection of melanoma in dermoscopy images using a pre a trained Deep Residual Network (ResNet).The model utilizes transfer learning to transfer the ResNet model for binary classification of skin lesions, between malignant melanoma and benign disorders. Preprocessing of dermaoscopic images is performed using techniques such as resizing, normalization, and data augmentation to improve model generalization.The ResNet model is trained and evaluated on a publicly accessible annotated data set. dataset, whose performance has been determined by metrics like accuracy, precision, recall, F1-score. The model’s ability at classification is enhanced, which is predictive efficiency and reliability of the deep residual networks in medical image analysis. The proposed The device offers a non-invasive and scalable solution that can be used by dermatologists and enhance melanoma early diagnosis, particularly in remote or low-resource medical environments.. Prospective improvements will emphasize the integration of interpretable artificial intelligence. methods and incorporating the model into portable diagnostic tools to facilitate real-time clinical Help.