Abstract
Diabetic Foot Ulcer is one of the most common diabetic complications that can lead to amputation if not treated appropriately and timely. When diagnosed by professionals, diabetic foot ulcers can be extremely successful, but the diagnosis comes at a great cost. Therefore, early automated detection tools are required to help diabetic people. In the current work, we test the ability of deep learning autoencoders to extract appropriate features that can be fed to machine learning algorithms to classify normal or abnormal skin areas. The proposed model was trained and tested on 754-foot photos of healthy and diabetic ulcer-affected skin from several individuals. By benchmarking various machine learning algorithms, our extensive research and experiments showed that the features extracted from autoencoder models can generate high accuracy when passed to a support vector machine with a polynomial kernel for early diagnosis, with 0.933 and 0.939 for accuracy and F1 score, respectively.
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
Kaul, K., Tarr, J.M., Ahmad, S.I., Kohner, E.M., Chibber, R.: Introduction to diabetes mellitus. In: Ahmad, S.I. (ed.) Diabetes: An Old Disease, a New Insight, pp. 1–11. Springer New York, New York, NY (2013). https://doi.org/10.1007/978-1-4614-5441-0_1
Saberzadeh-Ardestani, B., et al.: Type 1 diabetes mellitus: cellular and molecular pathophysiology at a glance. Cell J. (Yakhteh) 20(3), 294 (2018)
Chatterjee, S., Khunti, K., Davies, M.J.: Type 2 diabetes. The Lancet 389(10085), 2239–2251 (2017)
Khan, R.M.M., Chua, Z.J.Y., Tan, J.C., Yang, Y., Liao, Z., Zhao, Y.: From pre-diabetes to diabetes: diagnosis, treatments and translational research. Medicina 55(9), 546 (2019)
Tesfaye, S.: Neuropathy in diabetes. Medicine 43(1), 26–32 (2015)
Bus, S.A., Ret al.: Footwear and offloading interventions to prevent and heal foot ulcers and reduce plantar pressure in patients with diabetes: a systematic review. Diabetes/Metabol. Res. Rev. 32, 99–118 (2016)
Skrepnek, G.H., Mills, J.L., Sr., Lavery, L.A., Armstrong, D.G.: Health care service and outcomes among an estimated 6.7 million ambulatory care diabetic foot cases in the US. Diabetes Care 40(7), 936–942 (2017)
Subasi, A.: Chapter 3 - Machine learning techniques. In: Subasi, A. (ed.) Practical Machine Learning for Data Analysis Using Python, pp. 91–202. Academic Press (2020)
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(5), 728–739 (2020)
Das, S.K., Roy, P., Mishra, A.K.: DFU_SPNet: a stacked parallel convolution layers based CNN to improve Diabetic Foot Ulcer classification. ICT Express (2021)
Yap, M.H., et al.: Deep learning in diabetic foot ulcers detection: a comprehensive evaluation. Comput. Biol. Med. 135, 104596 (2021)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Presented at the Proceedings of the 28th International Conference on Neural Information Processing Systems, vol. 1. Montreal, Canada (2015)
Cassidy, B., et al.: The DFUC 2020 dataset: analysis towards diabetic foot ulcer detection. TouchREVIEWS in endocrinology 17(1), 5–11 (2021)
Khandakar, A., et al.: A machine learning model for early detection of diabetic foot using thermogram images. Comput. Biol. Med. 137, 104838 (2021)
Scebba, G., et al.: Detect-and-segment: A deep learning approach to automate wound image segmentation. Inform. Med. Unlocked 29, 100884 (2022)
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)
Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y.: Robust application of new deep learning tools: an experimental study in medical imaging. Multimedia Tools Appl. 81(10), 13289–13317 (2022). https://doi.org/10.1007/s11042-021-10942-9
Alzubaidi, L., et al.: Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. 10(13), 4523 (2020)
Orru, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36(4), 1140–1152 (2012)
Sarica, A., Cerasa, A., Quattrone, A.: Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review. Front. Aging neurosci. 9, 329 (2017)
Salmi, N., Rustam, Z.: Naïve Bayes classifier models for predicting the colon cancer. In: IOP Conference Series: Materials Science and Engineering, vol. 546, no. 5, p. 052068. IOP Publishing (2019)
Christodoulou, E., Ma, J., Collins, G.S., Steyerberg, E.W., Verbakel, J.Y., Van Calster, B.: A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 12–22 (2019)
Lopez Pinaya, W.H., Vieira, S., Garcia-Dias, R., Mechelli, A.: Chapter 11 – Autoencoders. In: Mechelli, A., Vieira, S. (eds.) Machine Learning, pp. 193–208. Academic Press (2020)
Alatrany, A., Hussain, A., Mustafina, J., Al-Jumeily, D.: A novel hybrid machine learning approach using deep learning for the prediction of alzheimer disease using genome data. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Premaratne, P. (eds.) ICIC 2021. LNCS (LNAI), vol. 12838, pp. 253–266. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84532-2_23
Alatrany, A.S., Hussain, A., Jamila, M., Al-Jumeiy, D.: Stacked machine learning model for predicting alzheimer's disease based on genetic data. In: 2021 14th International Conference on Developments in eSystems Engineering (DeSE), 7–10 Dec 2021, pp. 594–598 (2021). https://doi.org/10.1109/DeSE54285.2021.9719449.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Alatrany, A.S., Hussain, A., Alatrany, S.S.J., Al-Jumaily, D. (2022). Application of Deep Learning Autoencoders as Features Extractor of Diabetic Foot Ulcer Images. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-13832-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13831-7
Online ISBN: 978-3-031-13832-4
eBook Packages: Computer ScienceComputer Science (R0)