A Smart IoT-Image Processing System for Real-Time Skin Cancer Detection - Kumar, Sunny; Dwivedi, Apoorva; Perwej, Yusuf; Haidari, Moazzam; Singh, Siddharth; Gurusamy, Nagarajan (Journal of Neonatal Surgery, 2025)
Skin cancer represents a significant global health issue, with early detection being crucial for effective treatment and improved patient outcomes. Traditional methods of skin cancer diagnosis, including clinical examination and biopsy, are often invasive, time-consuming, and prone to human error. This paper proposes a smart IoT-based image processing system for real-time skin cancer detection, aimed at overcoming the limitations of current methods. The system integrates Internet of Things (IoT) devices for continuous monitoring and utilizes deep learning algorithms for accurate image analysis. The proposed system captures dermoscopic images, processes them using a convolutional neural network (CNN), and provides real-time classification of skin lesions into malignant or benign categories. Key findings show that the system achieved high accuracy and sensitivity, with a significant reduction in detection time compared to traditional approaches. The system's portability and real-time capabilities make it suitable for use in remote areas and for telemedicine applications, offering an accessible and reliable solution for early skin cancer detection. The results indicate that such a system could significantly improve the efficiency of skin cancer screening and contribute to better patient outcomes in clinical practice.