Classification of diabetic foot ulcer using convolutional neural network (CNN) in diabetic patients

The image of chronic wounds on human skin tissue has the similar look in shape, color and size to each other even though they are caused by different diseases. Diabetic ulcer is a condition where peripheral arterial blood vessels are disrupted due to hyperglycemia in people with diabetes mellitus. This research was aimed to analyze the accuracy of the Convolutional Neural Network algorithm in classifying diabetic ulcer disease with a transfer learning approach based on the appearance of the image of the wound on the sole in people with diabetes mellitus. By applying the transfer learning approach, the results showed that the Resnet152V2 model achieved the best accuracy value of 0.993 (99%), precision of 1.00, recall of 0.986, F1-Score of 0.993 and Support of 72. Therefore, the ResNet152V2 model was highly considered for classifying diabetic ulcer in patients with diabetes melitus.Citra luka kronis pada jaringan kulit manusia memiliki bentuk, warna dan besar luka yang terlihat menyerupai satu sama lain walau ditimbulkan oleh penyakit berbeda. Ulkus diabetik adalah kondisi dimana pembuluh darah arteri perifer terganggu disebabkan oleh hiperglikemia pada penderita Diabetes Melitus. Penelitian ini bertujuan untuk menganalisa  akurasi yang dihasilkan algoritma Convolutional Neural Network dalam mengklasifikasi penyakit ulkus diabetik dengan pendekatan transfer learning berdasarkan penampakan citra luka pada telapak kaki pada penderita diabetes melitus. Dengan menerapkan pendekatan transfer learning, diperoleh hasil bahwa model Resnet152V2 meraih nilai akurasi terbaik yakni sebesar 0.993 (99%), precision 1.00, recall 0.986, F1-Score 0.993 dan Support sebesar 72. Oleh sebab itu, model ResNet152V2 sangat dipertimbangkan untuk mengklasifikasikan penyakit ulkus diabetik pada penderita diabetes melitus

A Mobile App for Wound Localization using Deep Learning

We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system. The wound localizer has been developed by using YOLOv3 model, which is then turned into an iOS mobile application. The developed localizer can detect the wound and its surrounding tissues and isolate the localized wounded region from images, which would be very helpful for future processing such as wound segmentation and classification due to the removal of unnecessary regions from wound images. For Mobile App development with video processing, a lighter version of YOLOv3 named tiny-YOLOv3 has been used. The model is trained and tested on our own image dataset in collaboration with AZH Wound and Vascular Center, Milwaukee, Wisconsin. The YOLOv3 model is compared with SSD model, showing that YOLOv3 gives a mAP value of 93.9%, which is much better than the SSD model (86.4%). The robustness and reliability of these models are also tested on a publicly available dataset named Medetec and shows a very good performance as well.Comment: 8 pages, 5 figures, 1 tabl

Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades

Currently, the screening of Wagner grades of diabetic feet (DF) still relies on professional podiatrists. However, in less-developed countries, podiatrists are scarce, which led to the majority of undiagnosed patients. In this study, we proposed the real-time detection and location method for Wagner grades of DF based on refinements on YOLOv3. We collected 2,688 data samples and implemented several methods, such as a visual coherent image mixup, label smoothing, and training scheduler revamping, based on the ablation study. The experimental results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95% and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla V100. To test the performance of the model on a smartphone, we deployed the refinements on YOLOv3 models on an Android 9 system smartphone. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.Comment: 11 pages with 11 figure

Novel Computerised Techniques for Recognition and Analysis of Diabetic Foot Ulcers

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of Diabetes Mellitus (DM). It has been estimated that patients with diabetes have a lifetime risk of 15% to 25% in developing DFU contributing up to 85% of the lower limb amputation due to failure to recognise and treat DFU properly. Current practice for DFU screening involves manual inspection of the foot by podiatrists and further medical tests such as vascular and blood tests are used to determine the presence of ischemia and infection in DFU. A comprehensive review of computerized techniques for recognition of DFU has been performed to identify the work done so far in this field. During this stage, it became clear that computerized analysis of DFU is relatively emerging field that is why related literature and research works are limited. There is also a lack of standardised public database of DFU and other wound-related pathologies. We have received approximately 1500 DFU images through the ethical approval with Lancashire Teaching Hospitals. In this work, we standardised both DFU dataset and expert annotations to perform different computer vision tasks such as classification, segmentation and localization on popular deep learning frameworks. The main focus of this thesis is to develop automatic computer vision methods that can recognise the DFU of different stages and grades. Firstly, we used machine learning algorithms to classify the DFU patches against normal skin patches of the foot region to determine the possible misclassified cases of both classes. Secondly, we used fully convolutional networks for the segmentation of DFU and surrounding skin in full foot images with high specificity and sensitivity. Finally, we used robust and lightweight deep localisation methods in mobile devices to detect the DFU on foot images for remote monitoring. Despite receiving very good performance for the recognition of DFU, these algorithms were not able to detect pre-ulcer conditions and very subtle DFU. Although recognition of DFU by computer vision algorithms is a valuable study, we performed the further analysis of DFU on foot images to determine factors that predict the risk of amputation such as the presence of infection and ischemia in DFU. The complete DFU diagnosis system with these computer vision algorithms have the potential to deliver a paradigm shift in diabetic foot care among diabetic patients, which represent a cost-effective, remote and convenient healthcare solution with more data and expert annotations

Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: A systematic review

This is the final version. Available on open access from IEEE via the DOI in this record. Diabetic foot ulcers (DFUs) are a serious complication for people with diabetes. They result in increased morbidity and pressures on health system resources. Developments in machine learning (ML) offer an opportunity for improved care of individuals at risk of DFUs, to identify and synthesise evidence about the current uses and accuracy of ML in the interventional care and management of DFUs, and, to provide a reference for areas of future research. PubMed, Google Scholar, Web of Science and Scopus were searched using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for papers involving ML and DFUs. In order to be included, studies needed to mention ML, DFUs, and report relevant outcome measures regarding ML algorithm accuracy. Bias in included studies was assessed using the quality assessment tool for diagnostic accuracy (QUADAS2). 37 out of 3769 papers were included after applying eligibility criteria. Included papers reported accuracy measures for multiple types of ML algorithms in DFU studies. Whilst varying across the ML algorithm used, all studies reported at least 90% accuracy compared to gold standards using a minimum of one reported ML algorithm for processing or recording data. Applications where ML had positive effects on DFU data analysis and outcomes include image segmentation and classification, raw data analysis and risk assessment. ML offers an effective and accurate solution to guide analysis and procurement of data from interventions which are designed for the care of DFUs in small samples and study conditions. Current research is limited, and, for the development of more applicable ML algorithms, future research should address the following: direct comparison of ML applications with current standards of care, health economic analyses and large scale data collection. There is currently no evidence to confidently suggest that ML methods in DFU diagnosis are ready for implementation and use in healthcare settings

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques

© 2020 The Authors Recognition and analysis of Diabetic Foot Ulcers (DFU) using computerized methods is an emerging research area with the evolution of image-based machine learning algorithms. Existing research using visual computerized methods mainly focuses on recognition, detection, and segmentation of the visual appearance of the DFU as well as tissue classification. According to DFU medical classification systems, the presence of infection (bacteria in the wound) and ischaemia (inadequate blood supply) has important clinical implications for DFU assessment, which are used to predict the risk of amputation. In this work, we propose a new dataset and computer vision techniques to identify the presence of infection and ischaemia in DFU. This is the first time a DFU dataset with ground truth labels of ischaemia and infection cases is introduced for research purposes. For the handcrafted machine learning approach, we propose a new feature descriptor, namely the Superpixel Colour Descriptor. Then we use the Ensemble Convolutional Neural Network (CNN) model for more effective recognition of ischaemia and infection. We propose to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Finally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus non-infection. Overall, our method performed better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification

Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation

Objective: Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers. Methods: The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed. Results: Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters). Conclusions: We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development