Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

Ikramullah Khosa; Awais Raza; Mohd Anjum; Waseem Ahmad; Sana Shahab

doi:10.3390/diagnostics13162637

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

Diagnostics (Basel). 2023 Aug 10;13(16):2637. doi: 10.3390/diagnostics13162637.

Authors

Ikramullah Khosa¹, Awais Raza¹, Mohd Anjum², Waseem Ahmad³, Sana Shahab⁴

Affiliations

¹ Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
² Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India.
³ Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut 250005, India.
⁴ Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Abstract

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image-patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image-patch thermograms.

Keywords: deep learning; diabetes mellitus; diabetic foot ulcer; machine learning; thermograms.

Grants and funding

This research was supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R259), Princess Nourah bint Abdulrahman University, Saudi Arabia.