Citation :

S. Saint Jesudoss, K. Suresh Joseph, "Clinical Decision Support System for Diabetic Foot Ulcer Detection and Classification Model Using Fine-Grained Deep Transfer Learning Model," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 254-264, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P121

Abstract

Diabetic Foot Ulcers (DFU) remain a significant health issue for diabetes patients, necessitating early and accurate detection for rapid intervention. Because of insufficient blood circulation, the reappearance of these ulcers can result in 84% of lower limb amputation and even lead to mortality. Severe diabetes risk patients need costly medications, routine check-ups, and appropriate personal cleanliness to avoid DFUs that affect 15–25% of diabetics. Early detection, accurate diagnosis, proper care, and rapid response can eliminate amputations and death rates by early and robust DFU detection from image analysis. Therefore, efficient DRC detection is a necessity for enhanced patient care. Recently, Deep Learning (DL) systems have attained substantial consideration in the prognosis and diagnosis of DFU detection through different medical imaging conditions. This study develops an Automated Diabetic Foot Ulcer Detection and Classification utilizing Snow Ablation Optimization with Deep Learning (ADFUDC-SAODL) approach. The ADFUDC-SAODL approach enables early detection and classification of DFU leveraging a fine-grained DL approach. In the ADFUDC-SAODL approach, the MResCaps-based feature extraction approach is employed. The MResCaps method is an enhancement of the Capsule Network (CapsNet), which comprises a convolutional layer, a Primary Capsule (PC) layer, and a Digit Capsule (DigitCaps) layer. As the CapsNet model makes use of a single convolution layer, fundamental features of the image are extracted. To extract detailed features and enhance the classification efficiency of the CapsNet method, residual blocks are utilized rather than the convolution layer. Besides, a Convolutional Recurrent Neural Network (CRNN) approach was performed for the classification of DFU. At last, the Snow Ablation Optimization (SAO) method adjusts the hyperparameter values of the CRNN method optimally and leads to enhanced classification performance. An extensive set of simulations was involved to exhibit the encouraging outcomes of the ADFUDC-SAODL methodology. The simulated outcomes implied the proficient performance of the ADFUDC-SAODL methodology across other recent models.

Keywords

Deep Learning, Diabetic Foot Ulcer Detection, Snow Ablation Optimization, Image Pre-processing, Feature Extraction.

References

1. Moi Hoon Yap et al., “Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation,” Computers in Biology and Medicine, vol. 135, pp. 1-17, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Angelo Argenteri et al., “History of the Diagnosis and Treatment of Critical Limb Ischemia and Diabetic Foot,” Seminars in Vascular Surgery, vol. 31, no. 2-4, pp. 25-42, 2018.
   [CrossRef] [Google Scholar] [Publisher Link]
3. James C. Stanley, and Andrew M. Collier, “The Diabetic Foot and Ankle,” Orthopedie Traumatologie, vol. 23, no. 1, pp. 61-68, 2009.
   [CrossRef] [Google Scholar] [Publisher Link]
4. William A Wood, and Michael A Wood, “Decompression of Peripheral Nerves for Diabetic Neuropathy in the Lower Extremity,” The Journal of Foot and Ankle Surgery, vol. 42, no. 5, pp. 268-275, 2003.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Muhammad Adam et al., “Computer Aided Diagnosis of Diabetic Foot using Infrared Thermography: A Review,” Computers in Biology and Medicine, vol. 91, pp. 326-336, 2017.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Rebecca M. Crocker et al., “Patient Perspectives on the Physical, Psycho-social, and Financial Impacts of Diabetic Foot Ulceration and Amputation,” Journal of Diabetes and its Complications, vol. 35, no. 8, pp. 1-5, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Emmanuel Navarro-Floresor, and Omar Cauli, “Quality of Life in Individuals with Diabetic Foot Syndrome,” Endocrine Metabolic & Immune Disorders - Drug Targets, vol. 20, no. 9, pp. 1365-1372, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
8. S. A. Bus et al., “IWGDF Guidance on the Prevention of Foot Ulcers in at-risk Patients with Diabetes,” Diabetes/Metabolism Research and Reviews, vol. 32, no. S1, pp. 16-24, 2016.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Pouya Saeedi et al., “Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9^th edition,” Diabetes Research and Clinical Practice, vol. 157, pp. 1-10, 2019.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Manu Goyal et al., “Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques,” Computers in Biology and Medicine, vol. 117, pp. 1-10, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
11. S. V. N. Murthy et al., “Automated Detection of Infection in Diabetic Foot Ulcer Using Pre-trained Fast Convolutional Neural Network with U++Net,” SN Computer Science, vol. 5, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Mohammad H. Alshayeji, Silpa ChandraBhasi Sindhu, and Sa'ed Abed, “Early Detection of Diabetic Foot Ulcers from Thermal Images using the Bag of Features Technique,” Biomedical Signal Processing and Control, vol. 79, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
13. T. Arumuga Maria Devi, and R. Hepzibai, “Diabetic Foot Ulcer Classification of Hybrid Convolutional Neural Network on Hyperspectral Imaging,” Multimedia Tools and Applications, vol. 83, pp. 55199-55218, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Sunnam Nagaraju et al., “Automated Diabetic Foot Ulcer Detection and Classification Using Deep Learning,” IEEE Access, vol. 11, pp. 127578-127588, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Sujit Kumar Das, Suyel Namasudra, and Arun Kumar Sangaiah, “HCNNet: Hybrid Convolution Neural Network for Automatic Identification of Ischaemia in Diabetic Foot Ulcer Wounds,” Multimedia Systems, vol. 30, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Sujit Kumar Das et al., “AESPNet: Attention Enhanced Stacked Parallel Network to Improve Automatic Diabetic Foot Ulcer Identification,” Image and Vision Computing, vol. 138, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Nouf F. Almufadi, Haifa F. Alhasson, and Shuaa S. Alharbi, “E-DFu-Net: An Efficient Deep Convolutional Neural Network Models for Diabetic Foot Ulcer Classification,” Biomolecules and Biomedicine, vol. 25, no. 2, pp. 445-460, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Abdul Rahaman Wahab Sait, and Ramprasad Nagaraj, “Diabetic Foot Ulcers Detection Model Using a Hybrid Convolutional Neural Networks–Vision Transformers,” Diagnostics, vol. 15, no. 6, pp. 1-22, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Pramod Singh Rathore et al., “A Feature Explainability-based Deep Learning Technique for Diabetic Foot Ulcer Identification,” Scientific Reports, vol. 15, pp. 1-21, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Raj Kumar Gudivaka et al., “Diabetic Foot Ulcer Classification Assessment Employing an Improved Machine Learning Algorithm,” Technology and Health Care, vol. 33, no. 4, pp.1645-1660, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Md ZakirHossain et al., “Advanced Classification of Diabetic Foot Ulcers Using Custom and Deep Learning Models,” 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA), Gwalior, India, pp. 1-6, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
22. Saswati Debnath et al., “Sustainable AI for Diabetic Foot Ulcer Detection: A Deep Learning Approach for Early Diagnosis,” Discover Applied Sciences, vol. 7, pp. 1-21, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
23. P. Abirami, and S.N.S. Rajini, “Detection of Tuberculosis Using Optimized Deep Learning Approach with Enhanced Selective Median (ESMF) Filter,” African Journal of Biological Sciences, vol. 6, no. 2, pp. 193-210, 2024.
    [Google Scholar]
24. Ahmed Naser, “A Proposed CLCOA Technique based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement,” Wasit Journal of Computer and Mathematics Science, vol. 3, no. 1, pp. 18-27, 2024.
    [Google Scholar]
25. Sümeyra Büşra Şengül, and İlker Ali Özkan, “MResCaps: Enhancing Capsule Networks with Parallel Lanes and Residual Blocks for High-performance Medical Image Classification,” International Journal of Imaging Systems and Technology, vol. 34, no. 4, pp. 1-16, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Bing-Rui Chen et al., “Real-time Arrival Picking of Rock Microfracture Signals based on Convolutional-recurrent Neural Network and its Engineering Application,” Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 3, pp. 761-777, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Jinyi Xie et al., “An Enhanced Snow Ablation Optimizer for UAV Swarm Path Planning and Engineering Design Problems,” Heliyon, vol. 10, no. 18, pp. 1-18, 2024
    [CrossRef] [Google Scholar] [Publisher Link]
28. Diabetic Foot Ulcer (DFU). [Online]. Available: https://www.kaggle.com/datasets/laithjj/diabetic-foot-ulcer-dfu
29. J. Yogapriya et al., “Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network,” Journal of Healthcare Engineering, vol. 2022, pp. 1-12, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Mehnoor Ahsan et al., “A Deep Learning Approach for Diabetic Foot Ulcer Classification and Recognition,” Information, vol. 14, no. 1, pp. 1-10, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Puneeth N. Thotad et al., “Diabetic Foot Ulcer Detection using Deep Learning Approaches,” Sensors International, vol. 4, pp. 1-9, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]