Abstract
The Diabetic Foot (DF) is threatening every diabetic patient’s health. Every year, more than one million people suffer amputation in the world due to lack of timely diagnosis of DF. Diagnosing DF at early stage is very essential. However, it is easy for inexperienced doctors to confuse Diabetic Foot Ulcer (DFU) wounds and other specific ulcer wounds when there is a lack of patients’ health records in underdeveloped areas. In this paper, we propose an efficient two-stage fusion network fusing global foot features and local wound features to classify DF images and non-DF images. In particular, we apply an object detection module to detect wounds, which assists in making decisions on classification. The fusion network combines two crucial kinds of features extracted from foot areas and wound areas. Our method is evaluated upon our dataset collected by Shanghai Municipal Eighth People’s Hospital. In the training-validation stage, we collect 1211 images for a 5-fold cross-validation. Our method can classify DF images and non-DF images with the area under the receiver operating characteristic curve (AUC) value of 94.87\(\%\), accuracy of 88.19\(\%\), sensitivity of 84.79\(\%\), specificity of 90.63\(\%\), and F1-score of 85.68\(\%\). With the great performance, the proposed algorithm has great potential in clinical auxiliary diagnosis.
This work was supported by the High Performance Computing Center of Shanghai University and Shanghai Engineering Research Center of Intelligent Computing System under Project 19DZ2252600.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdullah Mueen, Dr., Luan, S.: Early detection and prevention of diabetic foot (2013). https://www.cs.unm.edu/~mueen/diabeticfoot/Proposal.pdf
Alzubaidi, L., Fadhel, M.A., Oleiwi, S.R., Al-Shamma, O., Zhang, J.: DFU_QUTNET: diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools Appl. 79(21), 15655–15677 (2020)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
Chadwick, P.: Best practice in the management of diabetic foot ulcers and pressure ulcers on the foot. Prim. Health Care 31 (2021). https://doi.org/10.7748/phc.2021.e1686
Fraiwan, L., AlKhodari, M., Ninan, J., Mustafa, B., Saleh, A., Ghazal, M.: Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study. Biomed. Eng. Online 16(1), 1–19 (2017)
Goyal, M., Reeves, N.D., Davison, A.K., Rajbhandari, S., Spragg, J., Yap, M.H.: DFUNet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans. Emerg. Top. Comput. Intell. 4, 728–739 (2018)
Goyal, M., Reeves, N.D., Rajbhandari, S., Ahmad, N., Wang, C., Yap, M.H.: Recognition of ischaemia and infection in diabetic foot ulcers: dataset and techniques. Comput. Biol. Med. 117, 103616 (2020)
Goyal, M., Reeves, N.D., Rajbhandari, S., Yap, M.H.: Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J. Biomed. Health Inform. 23(4), 1730–1741 (2018)
Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S., Spragg, J.: Fully convolutional networks for diabetic foot ulcer segmentation. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 618–623. IEEE (2017)
Han, A., et al.: Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades. arXiv preprint arXiv:2006.02322 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kasbekar, P.U., Goel, P., Jadhav, S.P.: A decision tree analysis of diabetic foot amputation risk in Indian patients. Front. Endocrinol. 8, 25 (2017)
Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: DetNet: a backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)
Liu, C., Netten, J.J.V., Baal, J.G.V., Bus, S.A., Heijden, F.V.D.: Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis. J. Biomed. Opt. 20(2), 26003 (2015)
Madarasingha, K., et al.: Development of a system to profile foot temperatures of the plantar and the periphery. In: TENCON 2018–2018 IEEE Region 10 Conference, pp. 1928–1932. IEEE (2018)
van Netten, J.J., van Baal, J.G., Liu, C., van Der Heijden, F., Bus, S.A.: Infrared thermal imaging for automated detection of diabetic foot complications. SAGE Publications Sage CA, Los Angeles (2013). https://doi.org/10.1177/193229681300700504
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Rogers, L.C., Armstrong, D.G., Boulton, A.J., Freemont, A.J., Malik, R.A.: Malignant melanoma misdiagnosed as a diabetic foot ulcer. Diabetes Care 30(2), 444–445 (2007)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Vardasca, R., Magalhaes, C., Seixas, A., Carvalho, R., Mendes, J.: Diabetic foot monitoring using dynamic thermography and AI classifiers. In: Proceedings of the QIRT Asia, pp. 1–5 (2019)
Vilcahuaman, L., et al.: Automatic analysis of plantar foot thermal images in at-risk type II diabetes by using an infrared camera. In: Jaffray, D.A. (ed.) World Congress on Medical Physics and Biomedical Engineering. IP, vol. 51, pp. 228–231. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19387-8_55
Wang, F., et al.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2017)
Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Yap, M.H., et al.: Deep learning in diabetic foot ulcers detection: a comprehensive evaluation. Comput. Biol. Med. 135, 104596 (2021)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Song, A., Zhu, H., Liu, L., Song, Z., Jin, H. (2021). An Efficient Two-Stage Fusion Network for Computer-Aided Diagnosis of Diabetic Foot. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-91415-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91414-1
Online ISBN: 978-3-030-91415-8
eBook Packages: Computer ScienceComputer Science (R0)