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

journal contribution
submitted on 2024-04-25, 08:09 and posted on 2024-04-25, 08:10 authored by Amith Khandakar, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Tariq O. Abbas, Tanvir Alam, Mohamed Arselene Ayari, Zaid B. Mahbub, Rumana Habib, Tawsifur Rahman, Anas M. Tahir, Ahmad Ashrif A. Bakar, Rayaz A. Malik

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 experts, 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.

Other Information

Published in: Sensors
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/s22051793

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • Sidra Medicine
  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU
  • Weill Cornell Medicine - Qatar

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