Deep Learning-Driven Non-Invasive Diabetes Diagnosis Using Tongue Images

Diabetes mellitus is a chronic metabolic disorder affecting millions worldwide. Early detection is crucial for effective management; however, current diagnostic methods often involve invasive procedures or are costly. This research explores a novel non-invasive approach for diabetes detection utilizing tongue image analysis and convolutional neural networks (CNNs). The study investigates the potential of CNNs to extract and classify features such as tongue thickness, color, texture, and geometry from tongue images to differentiate between diabetic and non-diabetic individuals. The results demonstrate promising accuracy in diabetes detection, comparable to traditional diagnostic methods. While further validation and clinical trials are necessary, this study highlights the potential of AI-powered tongue image analysis as a valuable tool for early diabetes screening, contributing to improved healthcare accessibility and patient outcomes.