Varicose Vein Detection and Real-Time Integration using Faster R-CNN Algorithm

Varicose veins are a prevalent vascular disorder affecting a significant portion of the population. This study aims to develop a user-friendly and cost-effective screening tool for early diagnosis and monitoring of varicose veins. A custom deep learning model was trained on a meticulously curated dataset of varicose vein images, categorizing them as "Normal" and "Varicose." The model's effectiveness was validated using a comprehensive dataset divided into training, testing, and validation. The system also extends to realtime varicose vein detection through laptop cameras, providing instant visual feedback for timely intervention. This real-time capability is complemented by long-term monitoring, making it a valuable tool for both clinical and home-based use. The proposed solution aligns with modern healthcare requirements, enabling early intervention in varicose vein management and contributing to improved healthcare outcomes.