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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Abstract

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care.

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Data Availability

The works used publicly available datasets mentioned in reference [19].

References

  1. R. Mostafiz, Diagnosis of diabetes: a machine learning paradigm using optimized features. Netw. Biol. 11(3), 222 (2021)

    Google Scholar 

  2. Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 12 May 2023

  3. A.J. Boulton, L. Vileikyte, G. Ragnarson-Tennvall, J. Apelqvist, The global burden of diabetic foot disease. Lancet 366(9498), 1719–1724 (2005)

    Article  PubMed  Google Scholar 

  4. C. Liu, J.J. van Netten, J.G. Van Baal, S.A. Bus, F. van Der Heijden, Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis. J. Biomed. Opt. 20(2), 026003 (2015)

    Article  ADS  Google Scholar 

  5. D.G. Armstrong, L.A. Lavery, L.B. Harkless, Validation of a diabetic wound classification system: the contribution of depth, infection, and ischemia to risk of amputation. Diabetes Care 21(5), 855–859 (1998)

    Article  CAS  PubMed  Google Scholar 

  6. P. Cavanagh, C. Attinger, Z. Abbas, A. Bal, N. Rojas, Z.-R. Xu, Cost of treating diabetic foot ulcers in five different countries. Diabetes Metab. Res. Rev. 28, 107–111 (2012)

    Article  PubMed  Google Scholar 

  7. F. Aguiree et al., IDF diabetes atlas, 6th edn., ed. L. Guariguata, T. Nolan, J. Beagley, U. Linnenkamp, O. Jacqmain (International Diabetes Federation, Brussels, 2013)

  8. G. Litjens et al., A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  PubMed  Google Scholar 

  9. F.J. Veredas, R.M. Luque-Baena, F.J. Martín-Santos, J.C. Morilla-Herrera, L. Morente, Wound image evaluation with machine learning. Neurocomputing 164, 112–122 (2015)

    Article  Google Scholar 

  10. L. Alzubaidi, M.A. Fadhel, O. Al-Shamma, J. Zhang, J. Santamaría, Y. Duan, Robust application of new deep learning tools: an experimental study in medical imaging. Multimed. Tools Appl. 81(10), 13289–13317 (2022)

    Article  Google Scholar 

  11. B. Najafi, H. Mohseni, G.S. Grewal, T.K. Talal, R.A. Menzies, D.G. Armstrong, An optical-fiber-based smart textile (smart socks) to manage biomechanical risk factors associated with diabetic foot amputation. J. Diabetes Sci. Technol. 11(4), 668–677 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  12. M. Kaselimi, E. Protopapadakis, A. Doulamis, N. Doulamis, A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring. Front. Physiol. 13, 924546 (2022)

    Article  PubMed  PubMed Central  Google Scholar 

  13. S.K. Das, P. Roy, A.K. Mishra, DFU_SPNet: a stacked parallel convolution layers based CNN to improve diabetic foot ulcer classification. ICT Express 8(2), 271–275 (2022)

    Article  Google Scholar 

  14. L. Alzubaidi, A.A. Abbood, M.A. Fadhel, O. Al-Shamma, J. Zhang, Comparison of hybrid convolutional neural networks models for diabetic foot ulcer classification. 16 (2021)

  15. L. Alzubaidi, M.A. Fadhel, S.R. Oleiwi, O. Al-Shamma, J. Zhang, DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed. Tools Appl. 79(21–22), 15655–15677 (2020)

    Article  Google Scholar 

  16. M. Goyal, N.D. Reeves, A.K. Davison, S. Rajbhandari, J. Spragg, M.H. Yap, Dfunet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans. Emerg. Top. Comput. Intell. 4(5), 728–739 (2018)

    Article  Google Scholar 

  17. L. Wang, P.C. Pedersen, E. Agu, D.M. Strong, B. Tulu, Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans. Biomed. Eng. 64(9), 2098–2109 (2017). https://doi.org/10.1109/TBME.2016.2632522

    Article  PubMed  Google Scholar 

  18. M. Goyal, M.H. Yap, N.D. Reeves, S. Rajbhandari, J. Spragg, Fully convolutional networks for diabetic foot ulcer segmentation, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, 2017), pp. 618–623

  19. Dataset: diabetic foot ulcer (DFU), Available link: https://www.kaggle.com/laithjj/diabetic-foot-ulcer-dfu

  20. L. Alzubaidi et al., Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. 10(13), 4523 (2020)

    Article  Google Scholar 

  21. N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res.Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  22. S.J. Pan, Q. Yang, A survey on transfer learning IEEE transactions on knowledge and data engineering. 22(10), 1345 (2009)

  23. J. Lu, V. Behbood, P. Hao, H. Zuo, S. Xue, G. Zhang, Transfer learning using computational intelligence: a survey. Knowl.-Based Syst..-Based Syst. 80, 14–23 (2015)

    Article  Google Scholar 

  24. Z. Huang, Z. Pan, B. Lei, Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sens. 9(9), 907 (2017)

    Article  ADS  Google Scholar 

  25. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Honolulu, 2017), pp. 2261–2269

  26. Z. Wu, C. Shen, A. Van Den Hengel, Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn.Recogn. 90, 119–133 (2019)

    Article  ADS  Google Scholar 

  27. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Las Vegas, NV, 2016) pp. 2818–2826

  28. F. Chollet, Xception: deep learning with depthwise separable convolutions, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Honolulu, HI, 2017) pp. 1800–1807

  29. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, MobileNetV2: inverted residuals and linear bottlenecks, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, Salt Lake City, UT, 2018) pp. 4510–4520

  30. G. Battineni, G.G. Sagaro, N. Chinatalapudi, F. Amenta, Applications of machine learning predictive models in the chronic disease diagnosis. JPM 10(2), 21 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  31. E.J. Topol, High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019)

    Article  CAS  PubMed  Google Scholar 

  32. A.M. Antoniadi et al., Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl. Sci. 11(11), 5088 (2021)

    Article  CAS  Google Scholar 

  33. M.T. Ribeiro, S. Singh, C. Guestrin, ‘Why should I trust you?’ Explaining the predictions of any classifier, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 1135–1144

  34. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: visual explanations from deep networks via gradient-based localization, in Proceedings of the IEEE International Conference on Computer Vision (2017), pp. 618–626

  35. S.M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017)

  36. N. Ketkar, Deep Learning with Python (Apress, Berkeley, 2017). https://doi.org/10.1007/978-1-4842-2766-4

  37. O. Russakovsky et al., ImageNet large scale visual recognition challenge (2015). Accessed 23 Sep 2023. Preprint at http://arxiv.org/abs/1409.0575

  38. A. Alqahtani, X. Xie, M.W. Jones, Literature review of deep network compression. Informatics 8(4), 77 (2021)

    Article  Google Scholar 

  39. D.P. Kingma, J. Ba, Adam: a method for stochastic optimization (2014). Preprint at http://arxiv.org/abs/1412.6980

  40. M.S. Santos, J.P. Soares, P.H. Abreu, H. Araujo, J. Santos, Cross-validation for imbalanced datasets: avoiding overoptimistic and overfitting approaches [Research Frontier]. IEEE Comput. Intell. Mag.Comput. Intell. Mag. 13(4), 59–76 (2018). https://doi.org/10.1109/MCI.2018.2866730

    Article  Google Scholar 

  41. S. Biswas, R. Mostafiz, B.K. Paul, K.M. Mohi Uddin, M.M. Rahman, F.N.U. Shariful, DFU_MultiNet: a deep neural network approach for detecting diabetic foot ulcers through multi-scale feature fusion using the DFU dataset. Intell.-Based Med. 8, 100128 (2023)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh

    Shuvo Biswas & Bikash Kumar Paul

  2. Institute of Information Technology, Noakhali Science and Technology University, Noakhali, Bangladesh

    Rafid Mostafiz

  3. Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh

    Bikash Kumar Paul

  4. Department of Computer Science and Engineering, Dhaka International University, Dhaka, Bangladesh

    Khandaker Mohammad Mohi Uddin

  5. Department of Computer Science: Information Technology, University of Nebraska Omaha, Omaha, USA

    Md. Abdul Hadi

  6. Department of Mathematics, American International University – Bangladesh, Dhaka, Bangladesh

    Fahmida Khanom

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  1. Shuvo Biswas
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  2. Rafid Mostafiz
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Correspondence to Rafid Mostafiz.

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Biswas, S., Mostafiz, R., Paul, B.K. et al. DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability. Biomedical Materials & Devices (2024). https://doi.org/10.1007/s44174-024-00165-5

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