Foot Ulcers in Patients with Diabetes: History
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Unawareness of the risk associated with diabetic foot ulcers (DFU) is a significant contributing factor to the mortality of diabetic patients. Evolving technological advancements such as deep learning techniques can be used to predict the symptoms of diabetic foot ulcers as early as possible, which helps to provide effective treatment to DM patients. 

  • foot ulcer
  • deep learning
  • DRNN
  • U++net

1. Introduction

High-throughput computing and developments in the field of biotechnology consistently contribute to affordable data production and a faster way of analysing data using big data technology in biological research. The aim is to build a faster-responding framework that considers the fast-growing biotic data and provides quick an probable responses to basic queries in the medical and biological areas. To achieve efficiency and reliability, choosing the correct approach to identify a pattern and create an efficient model from the dataset given is essential. Research on the prognosis and treatment of diseases that pose a threat to human life is crucial, and diabetes mellitus (DM) is one of the most significant diseases falling under this category. Statistical data from national science of medicine showed that, as of 2019, the number of individuals with diabetes in India was estimated to be 77 million, and is projected to exceed 134 million by 2045, with approximately 57% of cases remaining undiagnosed. Type 2 diabetes, which constitutes the majority of cases, can result in complications affecting multiple organs, which can be broadly categorized into micro-vascular and macro-vascular complications. These complications significantly contribute to premature morbidity and mortality rates among diabetic individuals, leading to a reduced life expectancy, financial burden, and other costs, which impose a profound economic burden on the Indian healthcare system. The cause of diabetesis not fully understood, although researchers suggest that both genetic and environmental factors may contribute to its development [1]. Although DM cannot be cured, medication and drugs can assist in its management. The timely detection and management of diabetes mellitus can help prevent complications and reduce the risk of severe health problems [2]. It can be diagnosed in two ways. The first way is manual diagnosed by a medical practitioner and the second is by using automated instruments. Each way has its own advantages and disadvantages. Manual diagnosis enables healthcare providers to depend on their skills without the need for machine intervention [3]. However, in the initial stages of DM, symptoms may be so mild that even an experienced doctor may not be able to identify them [4].
Dysregulated glycemia can lead to both acute and chronic complications. Diabetes mellitus is associated with an increased predisposition to diabetic heart disease (DHD), a cardiac pathology specific to individuals with diabetes. DHD is associated with several health issues, such as high cholesterol and high blood sugar, significantly increasing the risk of stroke or heart attack [5]. However, people with diabetes may not experience any symptoms due to the nerve damage that affects the nerves controlling the heart. DHD is a leading cause of death among individuals with diabetes, particularly women, who are often underdiagnosed for heart disease. Young or diabetic women are especially vulnerable, and many heart attacks in women are not accurately diagnosed [6]. Studies have found that women with diabetes have a risk of sudden death equivalent to half of that in men suffering from diabetes. Patients with type 2 diabetes are at from two- to four-fold higher risk of developing DHD as compared to those without diabetes [7].
Diabetic foot ulcers are a common issue among many diabetic patients, and if left untreated, they can result in partial or total amputation [8]. Early detection and treatment of these ulcers can prevent their development. However, traditional methods of diagnosis can be inadequate, as diabetic patients often experience a gradual loss of sensation, making self-examination difficult. Furthermore, currently, no automated system exists for the timely identification of diabetic foot ulcers. Prior research has demonstrated an association between elevated temperature and the onset of these ulcers [9].
Various algorithms are employed for the timely detection of diabetic foot ulcers via temperature evaluation. These algorithms encompass infrared images, liquid crystal thermography (LCT), and an infrared (IR) thermometer, and the temperature sensors are incorporated in the weighing scale. To predict DHD at an early stage, various data mining technologies and machine learning techniques are employed [10]. With the advancement of machine learning (ML) and artificial intelligence (AI), an automated program that assists in the early detection and diagnosis of diseases is becoming more viable and efficient than manual methods. The benefits of using automated programs include reducing the workload of medical professionals and minimizing the risk of human error [11]. Computer-based decision support systems can play a crucial rolein facilitating effective diagnosis and management.

2. Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes

Research studies indicate that skin temperature variations can be measured through the use of thermal images to detect diabetic foot ulcer infection. A number of authors have utilized thermal foot images to distinguish diabetic foot conditions characterized by sympathetic skin reaction from those without a sympathetic skin reaction [12]. Furthermore, the findings of this study demonstrate thermal foot imaging’s efficiency in detecting the presence of diabetic foot diseases, monitoring their progression and evaluating the effectiveness of related treatments [13]. A method of achieving this involves the division of images into six regions of interest (ROIs): arch, heel, forehead, lateral sole, hallux, and lesser toes, followed by the mean calculation of temperature differences for comparison with healthy participants’ temperatures. In a previous study, the authors proposed a system that utilized thermal images to diagnose diabetic foot conditions through the computation of corresponding point temperatures in both feet [14]. The findings indicated that the average temperature among patients with neuropathy ranged between 32.8 and 27.9 °C. Additionally, in another study, the author presented a system for detecting diabetic foot ulcers, which entailed the application of image-processing techniques such as segmentation, registration, and abnormality detection for thermal images. The process of segmenting the foot image involved the utilization of active contours, while background removal was achieved through the application of B-spline non-rigid registration, resulting in alignment [15]. The final step in the process involved the detection of abnormalities by subtracting the level of intensity in the corresponding position from the left foot to the right foot. Specifically, if the temperature difference exceeds a predefined threshold, it is deemed indicative of an abnormal region, as per the findings of this study.In another study, infrared images were employed for the discrimination of diabetic foot ulcer classes: those with no visible signs, those with local complications, and those with diffuse complications [16]. The mean temperature difference between the ipsilateral and contralateral foot can be calculated to accomplish this. The researcher utilized thermal imaging to detect abnormalities in diabetic foot conditions by correlating area temperatures with color codes. The approach entailed employing a rainbow palette, which was segmented into 10 distinct colors based on differences between them [17]. MATLAB mobile platform and Android smartphone were employed in the study to create an Android-based thermal system. Based on a comparison with a normal test image, the system predicted ulcers from four different ROI areas. Despite this, the study did not focus on cases that carried a high rate of complications, relying only on static thermal information in predicting ulcers in diabetics with grade-zero foot conditions [18]. The use of infrared images for detecting diabetic foot conditions through the application of image processing techniques was explored in a previous study [19], which reported the highest accuracy rate of 95.66%. However, the latitude of this research was limited to detect abnormalities. In the study conducted by [20], they utilized an alternate convolutional neural network to classify diabetic foot ulcers. Meanwhile, in [21], a thermal system was proposed to detect abnormalities in diabetic foot by extracting the textural and entropy features from the decomposed discrete wavelet transform (DWT) and higher-order spectra (HOS). However, the study was limited in that it could not distinguish between different types of diabetic foot conditions, achieving an identification rate of 89.39%. In [22], the authors focused on developing an ensemble model that utilized a majority voting technique to combine unweighted predictions from various machine learning models. On the other hand, the authors of [23] focused on discriminating techniques, where they combined the basic classifiers through a process that could adjust to the input observations and output requirements of each individual learning system.In the training set, the type of combination employed could be optimized by assigning weight to each classifier in order to improve the combined performance. In a study by the authors of [24], ensemble-based methods were recommended as the most effective approach for data stream classification problems. Furthermore, the authors compared ensemble learning with sixty other algorithms. Similarly, the author of [25] notes that in the realm of machine learning, multiple classifier systems have undergone considerable development in recent years, presenting promising solutions to a range of problems and enhancing precision.

This entry is adapted from the peer-reviewed paper 10.3390/diagnostics13121983


  1. 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 2017, 16, 117.
  2. Thotad, P.N.; Bharamagoudar, G.R.; Anami, B.S. Diabetic foot ulcer detection using deep learning approaches. Sens. Int. 2023, 4, 100210.
  3. Ahsan, M.; Naz, S.; Ahmad, R.; Ehsan, H.; Sikandar, A. A deep learning approach for diabetic foot ulcer classification and recognition. Information 2023, 14, 36.
  4. Purnima, S.; Angelin, P.S.; Priyanka, R.; Subasri, G.; Venkatesh, R. Automated Detection of Diabetic Foot Using Thermal Images by Neural Network Classifiers. Int. J. Emerg. Trends Sci. Technol. 2017, 4, 6.
  5. Xu, Y.; Han, K.; Zhou, Y.; Wu, J.; Xie, X.; Xiang, W. Classification of diabetic foot ulcers using class knowledge banks. Front. Bioeng. Biotechnol. 2021, 9, 811028.
  6. Haque, F.; Reaz, M.B.I.; Chowdhury, M.E.H.; Ezeddin, M.; Kiranyaz, S.; Alhatou, M.; Ali, S.H.M.; Bakar, A.A.A.; Srivastava, G. Machine learning-based diabetic neuropathy and previous foot ulceration patients detection using electromyography and ground reaction forces during gait. Sensors 2022, 22, 3507.
  7. Aiello, E.M.; Toffanin, C.; Messori, M.; Cobelli, C.; Magni, L. Postprandial glucose regulation via KNN meal classification in type 1 diabetes. IEEE Control Syst. Lett. 2018, 3, 230–235.
  8. Raza, A.; Ayub, H.; Khan, J.A.; Ahmad, I.; Salama, A.S.; Daradkeh, Y.I.; Javeed, D.; Ur Rehman, A.; Hamam, H. A hybrid deep learning-based approach for brain tumor classification. Electronics 2022, 11, 1146.
  9. Carracedo, J.; Alique, M.; Ramírez-Carracedo, R.; Bodega, G.; Ramírez, R. Endothelial extracellular vesicles produced by senescent cells: Pathophysiological role in the cardiovascular disease associated with all types of diabetes mellitus. Curr. Vasc. Pharmacol. 2019, 17, 447–454.
  10. Sadiq, M.T.; Yu, X.; Yuan, Z.; Zeming, F.; Rehman, A.U.; Ullah, I.; Li, G.; Xiao, G. Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces. IEEE Access 2019, 7, 171431–171451.
  11. Ahmad, I.; Ullah, I.; Khan, W.U.; Ur Rehman, A.; Adrees, M.S.; Saleem, M.Q.; Cheikhrouhou, O.; Hamam, H.; Shafiq, M. Efficient algorithms for E-healthcare to solve multiobject fuse detection problem. J. Healthc. Eng. 2021, 2021, 9500304.
  12. Sadiq, M.T.; Yu, X.; Yuan, Z.; Fan, Z.; Rehman, A.U.; Li, G.; Xiao, G. Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform. IEEE Access 2019, 7, 127678–127692.
  13. Jaiswal, V.; Negi, A.; Pal, T. A review on current advances in machine learning based diabetes prediction. Prim. Care Diabetes 2021, 15, 435–443.
  14. Muthuraja, M.; Shanthi, N. A Survey on Classification of Diabetic Foot Ulcer Using Machine Learning Algorithms. Ann. Rom. Soc. Cell Biol. 2021, 25, 1881–1894.
  15. Cruz-Vega, I.; Gutiérrez-Rodríguez, A.K.; Sánchez-Pérez, H.J.; García-Rodríguez, M.L. Deep learning classification for diabetic foot thermograms. Sensors 2020, 20, 1762.
  16. Muñoz, P.L.; Rodríguez, R.; Montalvo, N. Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks. J. Biomed. Res. Clin. Investig. 2020, 1, 1006.
  17. Maheswari, D.; Kayalvizhi, M. A Hybrid Deep Learning Algorithms for Diabetes Mellitus Prediction Using Thermal Foot Images. Eur. J. Mol. Clin. Med. 2021, 7, 5176–5183.
  18. Islam, M.M.; Ferdousi, R.; Rahman, S.; Bushra, H.Y. Likelihood prediction of diabetes at early stage using data mining techniques. In Computer Vision and Machine Intelligence in Medical Image Analysis; Springer: Berlin/Heidelberg, Germany, 2020; pp. 113–125.
  19. Chaki, J.; Ganesh, S.T.; Cidham, S.K.; Theertan, S.A. Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review. J. King Saud-Univ.-Comput. Inf. Sci. 2020, 34, 3204–3225.
  20. Abaker, A.A.; Saeed, F.A. A Comparative Analysis of Machine Learning Algorithms to Build a Predictive Model for Detecting Diabetes Complications. Informatica 2021, 45, 1–10.
  21. Ramsingh, J.; Bhuvaneswari, V. An efficient Map Reduce-Based Hybrid NBC-TFIDF algorithm to mine the public sentiment on diabetes mellitus—A big data approach. J. King Saud-Univ.-Comput. Inf. Sci. 2018, 33, 1018–1029.
  22. Natarajan, S.; Jain, A.; Krishnan, R.; Rogye, A.; Sivaprasad, S. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol. 2019, 137, 1182–1188.
  23. Younus, M.; Munna, M.T.A.; Alam, M.M.; Allayear, S.M.; Ara, S.J.F. Prediction model for prevalence of type-2 diabetes mellitus complications using machine learning approach. In Data Management and Analysis; Springer: Berlin/Heidelberg, Germany, 2020; pp. 103–116.
  24. Mainenti, G.; Campanile, L.; Marulli, F.; Ricciardi, C.; Valente, A.S. Machine Learning Approaches for Diabetes Classification: Perspectives to Artificial Intelligence Methods Updating. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), Prague, Czech Republic, 7–9 May 2020.
  25. Mwawado, R.H. Development of a Fast and Accurate Method for the Segmentation of Diabetic Foot Ulcer Images. Ph.D. Thesis, NM-AIST, Arusha, Tanzania, 2020.
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