Early Detection of Diabetic Complications Using Thermal Image Analysis and Deep Learning

Diabetes is a chronic metabolic disorder that can lead to several serious complications if not diagnosed at an early stage. Early identification of these complications is crucial for preventing severe health issues such as neuropathy, diabetic foot ulcers, and circulatory problems. Traditional diagnostic methods often require clinical examination and specialized equipment, which may not always be available in remote or resource-limited areas.This project proposes a deep learning-based approach for the early detection of diabetic complications using thermal image analysis. Thermal imaging captures temperature variations in body tissues, which can indicate abnormal blood circulation and inflammation associated with diabetic conditions. By analyzing these thermal patterns, potential complications can be identified at an early stage. A Convolutional Neural Network (CNN) model is used to automatically analyze thermal images and classify them into normal and diabetic complication categories. The dataset used for training and evaluation is obtained from Kaggle. Image preprocessing techniques such as resizing, normalization, and augmentation are applied to enhance model performance and improve classification accuracy. The trained model can assist healthcare professionals by providing a fast and reliable screening tool for early detection. This approach demonstrates the potential of combining thermal imaging with deep learning techniques to support medical diagnosis and improve patient outcomes.