Deep Learning-Based Classification of Abrasion and Ischemic Diabetic Foot Sores Using Camera-Captured Images

Mudassir Khalil, Ahmad Naeem, Rizwan Ali Naqvi, Kiran Zahra, Syed Atif Moqurrab () and Seung-Won Lee ()
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Mudassir Khalil: Department of Computer Engineering, Bahauddin Zakariya University, Multan 60000, Pakistan
Ahmad Naeem: Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
Rizwan Ali Naqvi: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Kiran Zahra: Division of Oncology, Washington University, St. Louis, MO 63130, USA
Syed Atif Moqurrab: School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Seung-Won Lee: School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea

Mathematics, 2023, vol. 11, issue 17, 1-21

Abstract: Diabetic foot sores (DFS) are serious diabetic complications. The patient’s weakened neurological system damages the tissues of the foot’s skin, which results in amputation. This study aims to validate and deploy a deep learning-based system for the automatic classification of abrasion foot sores (AFS) and ischemic diabetic foot sores (DFS). We proposed a novel model combining convolutional neural network (CNN) capabilities with Vgg-19. The proposed method utilized two benchmark datasets to classify AFS and DFS from the patient’s foot. A data augmentation technique was used to enhance the accuracy of the training. Moreover, image segmentation was performed using UNet++. We tested and evaluated the proposed model’s classification performance against two well-known pre-trained classifiers, Inceptionv3 and MobileNet. The proposed model classified AFS and ischemia DFS images with an accuracy of 99.05%, precision of 98.99%, recall of 99.01%, MCC of 0.9801, and f1 score of 99.04%. Furthermore, the results of statistical evaluations using ANOVA and Friedman tests revealed that the proposed model exhibited a remarkable performance. The proposed model achieved an excellent performance that assist medical professionals in identifying foot ulcers.

Keywords: ischemic; deep learning; diabetic foot score; CNN; abrasion; image segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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