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Diabetes foot ulceration (DFU) and amputation are a cause of significantmorbidity. The prevention of DFU may be achieved by the identification ofpatients at risk of DFU and the institution of preventative measures througheducation and offloading. Several studies have reported that thermogram imagesmay help to detect an increase in plantar temperature prior to DFU. However,the distribution of plantar temperature may be heterogeneous, making itdifficult to quantify and utilize to predict outcomes. We have compared amachine learning-based scoring technique with feature selection andoptimization techniques and learning classifiers to several state-of-the-artConvolutional Neural Networks (CNNs) on foot thermogram images and propose arobust solution to identify the diabetic foot. A comparatively shallow CNNmodel, MobilenetV2 achieved an F1 score of ~95% for a two-feet thermogramimage-based classification and the AdaBoost Classifier used 10 features andachieved an F1 score of 97 %. A comparison of the inference time for thebest-performing networks confirmed that the proposed algorithm can be deployedas a smartphone application to allow the user to monitor the progression of theDFU in a home setting.

A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images