DFU Infection and Ischemia Classification: PSO-Optimized Deep Learning Networks

Audrey Huong, Kim Gaik Tay, Nur Anida Jumadi, Wan Mahani Hafizah Wan Mahmud, Xavier Ngu


The recognition of infection and local perfusion (i.e., ischemic) status of diabetic foot ulcer (DFU) on a regular and timely basis is crucial to promote wound healing and prevent the development of unwanted complications. The conventional DFU assessment method is limited to scheduled clinic visits, impeding close monitoring of foot lesion progression and its chronicity. This paper presents an efficient Particle Swarm Optimization (PSO)-incorporated framework for classifying DFU infection and ischemia conditions using three deep learning models: AlexNet, GoogleNet, and EfficientNet-B0. The optimized system performed well in all evaluation metrics, ranging between 0.82 and 0.92 and near-perfect scores of 0.97 - 1, respectively, indicating the high performance and robustness of the system for the DFU infection and ischemia classification tasks. These results are better than the recent related studies using the same datasets. This system performs competitively with the deeper and heavier Efficient-B5 model, suggesting the efficiency of the proposed strategy without demanding an extensive network exploration process or elaborative feature selection process. The future of this work includes transferring the technology for DFU management using a mobile-based technology platform to improve outpatient care delivery through rapid recognition of DFU infection and their perfusion to optimize limb salvage outcomes.


Diabetic foot ulcer; EfficientNet; Infection; Ischemia; PSO.

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D. J. Magliano and E. J. Boyko, IDF Diabetes Atlas, International Diabetes Federation, 10th ed. Brussels, 2021.

N. Gundluru, D. S. Rajput, K. Lakshmanna, R. Kaluri, M. Shorfuzzaman, M. Uddin and M. A. R. Khan, Enhancement of detection of diabetic retinopathy using Harris Hawks optimization with deep learning model, Computational Intelligence and Neuroscience, 2022, 1-13.

A. Stanek, R. Małecki, K. Klimas and A. Kujawa, Different patterns of bacterial species and antibiotic susceptibility in diabetic foot syndrome with and without coexistent ischemia, Journal of Diabetes Research, 2021, 1-9.

R. R. Nadia, M. J. Indira, G. O. Ariana, M. M. Yssel, G. N. Gerardo, G. A. David and B. A. Jorge, Wound chronicity, impaired immunity and infection in diabetic patients, MEDICC Review, 24, 2022, 44-58.

K. Mponponsuo, R. G. Sibbald and R. Somayaji, A comprehensive review of the pathogenesis, diagnosis, and management of diabetic foot infections, Advances in Skin & Wound Care, 34, 2021, 574-581.

Q. Guo, G. Ying, O. Jing, Y. Zhang, Y. Liu, M. Deng and S. Long, Influencing factors for the recurrence of diabetic foot ulcers: A meta-analysis, International Wound Journal, 20, 2023, 1762‐1775.

Z. Liu, J. John and E. Agu, Diabetic Foot ulcer ischemia and infection classification using Efficientnet deep learning models, IEEE Open Journal of Engineering in Medicine and Biology, 3, 2022, 189-201.

M. Ahsan, S. Naz, R. Ahmad, H. Ehsan and A. Sikandar, A deep learning approach for diabetic foot ulcer classification and recognition, Information, 14, 36, 2023, 1-10.

M. Goyal, N. D. Reeves, S. Rajbhandari, N. Ahmad, C. Wang and M. H. Yap, Recognition of ischemia and infection in diabetic foot ulcers: Dataset and techniques, Computers in Biology and Medicine, 117, 2020, 1-10.

A. G. Nora, R. Ebsim, A. F. H. Alharan and M. H. Yap, Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks, Computers in Biology and Medicine, 140, 2021.

A. Huong, K. G, Tay, K. B. Gan and X. Ngu, A Hierarchical optimisation framework for pigmented lesion diagnosis, CAAI Transactions on Intelligence Technology, 1, 2022, 34-45.

M. H. Yap, C. Kendrick, N. D. Reeves, M. Goyal, J. M. Pappachan and B. Cassidy, Development of diabetic foot ulcer datasets: an overview, Diabetic Foot Ulcers Grand Challenge, 13183, 2021, 1–18.

M. Tan and Q. V. Le, EfficientNet: Rethinking model scaling for convolutional neural networks, Proceedings of the 36th International Conference on Machine Learning, Long Beach, 2019, 6105-6114.

H. L. Minh, S. Khatir, M. A. Wahab and T. C. Le, An enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower, Engineering Structures, 241, 2021, 1-23


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