A machine learning model for early detection of diabetic foot using thermogram images

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention DFU may be achieved by the identification patients at risk institution preventative measures through education offloading. Several studies have reported that thermogram images help to detect an increase in plantar temperature prior DFU. However, distribution heterogeneous, making it difficult quantify utilize predict outcomes. We compared machine learning-based scoring technique with feature selection optimization techniques learning classifiers several state-of-the-art Convolutional Neural Networks (CNNs) on propose robust solution identify diabetic foot. A comparatively shallow CNN model, MobilenetV2 F1 score ∼95% for two-feet image-based classification AdaBoost Classifier used 10 features 97%. comparison inference time best-performing networks confirmed proposed algorithm can deployed as smartphone application allow user monitor progression home setting.