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

TL;DR

This paper presents a machine learning approach using thermogram images to detect diabetic foot risks early, achieving high accuracy and enabling deployment as a smartphone app for home monitoring.

Contribution

It introduces a robust machine learning framework with feature selection and optimization for thermogram analysis, outperforming existing CNNs in diabetic foot detection.

Findings

MobilenetV2 achieved ~95% F1 score

AdaBoost classifier achieved 97% F1 score

Proposed method is suitable for smartphone deployment

Abstract

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