An Enhanced Technique for Skin Cancer Classification Using RCNN and Yolo Contours

Skin Cancer is a significant worldwide medical condition that calls need accurate and timely diagnostic approaches. Here in this work, we have suggested an advanced system for skin cancer prediction leveraging the YOLO Contour method and R-CNN. By integrating these cutting-edge deep learning techniques, our system aims to provide precise lesion localization and accurate classification, thereby enhancing early detection capabilities. We discuss the implementation of YOLO for contour detection and R-CNN for lesion classification, highlighting their synergistic benefits in improving diagnostic accuracy. Through comprehensive experimentation on diverse skin lesion datasets, we demonstrate the efficacy of our proposed system in achieving superior performance compared to existing methods. Our Findings highlight the possibilities of incorporating novel deep learning methods for enhancing skin cancer diagnosis, with consequences for enhancing patient outcomes and clinical decision-making.