Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

Amith Khandakar, Muhammad Chowdhury, Mamun Reaz, Sawal Ali, Tariq Abbas, Tanvir Alam, Mohamed Ayari, Zaid Mahbub, Rumana Habib, Tawsifur Rahman, Anas Tahir, Ahmad Bakar (+1 others)
2022 Sensors  
Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain
more » ... xperts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.
doi:10.3390/s22051793 pmid:35270938 pmcid:PMC8915003 fatcat:6zq5yotfb5abjped5f7zi5i3n4