<![CDATA[Skin cancer is a common global health issue, with the number of cases continuing to rise worldwide. Early detection is crucial for improving patient outcomes, but traditional detection methods often require significant time, cost, and medical expertise. To address this challenge, this research focuses on developing a mobile application that leverages deep learning, specifically the YOLOv10x model, to enable fast and accurate detection of skin lesions. This application aims to provide an easy-to-use platform for self-monitoring skin health, particularly for individuals in remote areas with limited access to medical facilities. The system uses the HAM10000 dataset, which consists of a diverse collection of dermoscopy images of skin lesions, to train the YOLOv10x object detection model for real-time detection on mobile devices. By leveraging TensorFlow.js and Node.js, the model processes skin images and provides real-time results with precision and efficiency. The mobile application, developed using the Ionic Framework, ensures cross-platform compatibility and a responsive, intuitive user interface. System performance was evaluated using key metrics such as Precision (84.2%), Recall (86.3%), mAP (89.2%), and F1 Score (85.2%), demonstrating its effectiveness in early skin cancer detection. The potential of this application extends beyond detection, contributing to society by raising awareness and offering an accessible, low-cost screening solution.]]>
Copyrights © 2025
