Journal of Multimedia Processing and Technologies K. Kiruthika 17 3 2026 https://doi.org/10.6025/jmpt/2026/17/3/124-151 https://www.dline.info/jmpt/fulltext/v17n3/jmptv17n3_2.pdf Background: Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, with early detection being critical for improving patient outcomes. Dermoscopic imaging has become an indispensable tool for dermatological assessment; however, manual interpretation is challenging due to substantial variability in lesion appearance, ambiguous boundaries, and common imaging artefacts. Automated computer-aided diagnostic systems have emerged as promising solutions to assist clinicians in the early detection and accurate assessment of skin lesions. Objective: This study presents a comprehensive end to end computational framework for automated skin lesion detection and instance segmentation using the Mask R-CNN architecture. The primary objectives are to develop a robust system that simultaneously performs lesion localization, classification, and pixel level segmentation while providing clinically interpretable outputs to support dermatological decision making. Methods: The proposed framework integrates a ResNet-50 backbone with a Feature Pyramid Network (FPN) for multi scale feature extraction, a Region Proposal Network (RPN) for candidate lesion detection, ROI Align for precise spatial feature extraction, and parallel multi task heads for classification, boundingbox regression, and mask prediction. The model was trained and evaluated on a combined dataset of 2,500 dermoscopic images sourced from the International Skin Imaging Collaboration (ISIC) Archive and the PH² dataset, with expert annotated ground truth including bounding boxes and binary segmentation masks. Performance was assessed using Dice coefficient, Intersection over Union (IoU), pixel accuracy, mean Average Precision (mAP), inference time, and training convergence. The framework was systematically compared against the U-Net++ semantic segmentation baseline. Results: The proposed Mask R-CNN framework achieved strong performance on the validation set, with a Dice coefficient of 0.912, an IoU of 0.847, a pixel accuracy of 0.968, and an mAP@0.5 of 0.893. These results represent significant improvements over the U-Net++ baseline, with relative gains of 4.3% in Dice coefficient and 6.9% in IoU. The model demonstrated robust boundary adherence across lesions of varying sizes and morphological characteristics while maintaining clinically acceptable inference speeds of approximately48 ms per image. Training converged efficiently over 15 epochs with a final loss of 0.227. Per class analysis revealed strong performance for both benign (Dice: 0.928) and malignant (Dice: 0.885) lesions. Statistical analysis using paired Wilcoxon signed rank tests confirmed the significance of performance improvements (p = 0.043, effect size d  1.12). Conclusion: The proposed Mask R-CNN framework provides a robust, accurate, and clinically viable solution for automated skin lesion detection and instance segmentation. By simultaneously delivering lesion classification, precise boundary delineation, and instance level segmentation masks, the system offers comprehensive morphological information that directly supports quantitative lesion assessment and computer assisted clinical decision making. While challenges remain in handling severe imaging artefacts and domain generalization, this work establishes Mask R-CNN as a powerful foundation for computer aided dermatological diagnosis, with substantial potential for integration into routine clinical practice to facilitate early melanoma detection and improve patient outcomes.