Deep Learning on Wound Segmentation and Classification: A Short Review and Evaluation of Methods Used

The abundance of research on wound segmentation suggests that it is significant in order to provide a good analysis and assistance in the medical field. Although there is some relative dearth of wound segmentation on other approaches, this review finds that deep learning is central to the objective of image segmentation. Here, the review informs on the methods that are credible towards wound segmentation, training, classification, validation of datasets, data collection, and evaluation of segmented images. While the literature establishes a clear connection between the segmentation algorithms of the object, therefore this study seeks to find the segmentation algorithm directly applicable to wound assessment

Soft White Tissue Detection From Pressure Ulcer Images Using Anisotropic Diffused Total Variation Fuzzy C Means

The goal of image segmentation is to cluster pixels into salient image regions. It can identify the regions of interest in an image or annotate the data. In medical imaging, these segments often correspond to different tissue classes, pathologies, or other biologically relevant structures. Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. The goal of segmentation of pressure ulcer images is to find out the level of tissue wound and soft white tissue present. Soft white tissue protein level changes are mostly found in elderly people. Soft white tissue present may be dark red or light yellow gel based on the different imaging modes of severity of pressure ulcer. This helps in diagnosing the disease and to plan for the treatment. The soft white tissue detection is made difficult for the segmentation because of the noise present in the image. Clustering techniques are best suited to segment the input images with noise. Clustering is usually performed when no information is available concerning to the membership of data items to predefined classes. For this reason clustering is traditionally seen as a part of unsupervised learning

DETEKSI KELILING LUKA KRONIS MENGGUNAKAN ACTIVE CONTOUR (SNAKE) DAN ACTIVE CONTOUR YANG DITAMBAHKAN INTERPOLASI

Luka kronis menjadi permasalahan bagi perawat luka dan instansi kesehatan terkait. Salah satu hal mendasar dalam penyembuhan luka kronis adalah melihat ukuran luka yang akan diamati dalam proses assesment luka yang saat ini masih dilakukan secara manual dan hal tersebut rentan dengan ketidakakuratan. Untuk mengatasi ketidakakuratan pengukuran manual, maka metode pengukuran keliling luka berbasis analisa citra (image), khususnya citra biomedis (biomedical image) dan citra medis (medical image) perlu dikembangkan. Skripsi ini bertujuan untuk mengimplementasi dan melihat hasil metode active contour (snake) dalam kasus deteksi keliling luka kronis. Implementasi yang dikembangkan menggunakan metode snake dengan snake yang ditambahkan interpolasi untuk deteksi keliling luka kategori luka hitam, kuning, dan merah. Hasil akhir menunjukkan bahwa data yang berhasil dideteksi menggunakan snake interpolasi (44 data dari 71 data) lebih banyak dibandingkan versi integer (12 data dari 71 data) dengan nilai akurasi rata-rata 77.18% untuk snake versi integer dan 86.1% untuk snake versi interpolasi. ***** Chronic wounds are a problem for wound nurses and related health agencies. One of the basic things in chronic wound healing is to see the size of the wound that will be observed in the wound assessment process, which is currently still done manually and is prone to inaccuracies. To overcome the inaccuracy of manual measurements, the method of measuring wound circumference based on image analysis, especially biomedical images and medical images, needs to be developed. This thesis aims to implement and see the results of the active contour (snake) method in the case of chronic wound circumference detection. The implementation developed uses the snake method with a snake added by interpolation to detect the circumference of the wound in the black, yellow, and red categories. The final result shows that the data detected using snake interpolation (44 data from 71 data) is more than the integer version (12 data from 71 data) with an average accuracy value of 77.18% for the integer version of the snake and 86.1% for the interpolated version of the snake

Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection

Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment. DFU patient population is on the rise and will soon outpace the available health resources. Autonomous monitoring and evaluation of DFU wounds is a much-needed area in health care. In this paper, we evaluate and identify the most accurate feature extractor that is the core basis for developing a deep-learning wound detection network. For the evaluation, we used mAP and F1-score on the publicly available DFU2020 dataset. A combination of UNet and EfficientNetb3 feature extractor resulted in the best evaluation among the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier in the development of a comprehensive DFU domain-specific autonomous wound detection pipeline.Comment: 5 pages, 2 figures, 3 tables, 2022 IEEE International Conference on Data Mining Workshop

SIM2PeD : intelligent monitoring system for prevention of diabetic foot

Diabetes is an endocrine chronic disease that causes high blood sugar level, produced by retardation in, or deficiency of, glucose metabolism in the body of the individual with the disease. Neuropathies and/or angiopathies are complications of diabetes that result in changes in the lower limbs which subsequently evolve for the diabetic foot. Diabetic foot represents one of the most devastating complications of diabetes and can lead to ulcerations, amputations and even death. Based on these, the aim of this work was to develop an Intelligent System for Monitoring the Prevention of Diabetic Foot (SIM2PeD), allowing personalized care from each individual routine. The work consists of a platform integrated with a mobile device to capture individuals’ data, entitled Mobile SIM2PeD, and a web device for monitoring the medical patient, titled Web SIM2PeD. Individuals receive alerts regarding care according to their location and activity directly from their smartphones. After capturing, the information is passed to the expert system (Intelligent module) that generates recommendations from the answers. The developed system presents a model of alerts as the best architecture, to the detriment of the pictogram model. The data captured show that slight displacements in frequency caused large variations of answers delivered to the application. The various experiments conducted made the system performed to be specified, and suitable for the remote monitoring of self-care activities in patients with diabetic foot

A new smart mobile system for chronic wound care management

Nonhealing wounds pose a major challenge in clinical medicine. Typical chronic wounds, such as diabetic foot ulcers and venous leg ulcers, have brought substantial difficulties to millions of patients around the world. The management of chronic wound care remains challenging in terms of precise wound size measurement, comprehensive wound assessment, timely wound healing monitoring, and efficient wound case management. Despite the rapid progress of digital health technologies in recent years, practical smart wound care management systems are yet to be developed. One of the main difficulties is in-depth communication and interaction with nurses and doctors throughout the complex wound care process. This paper presents a systematic approach for the user-centered design and development of a new smart mobile system for the management of chronic wound care that manages the nurse's task flow and meets the requirements for the care of different types of wounds in both clinic and hospital wards. The system evaluation and satisfaction review was carried out with a group of ten nurses from various clinical departments after using the system for over one month. The survey results demonstrated high effectiveness and usability of the smart mobile system for chronic wound care management, in contrast to the traditional pen-and-paper approach, in busy clinical contexts

Processing Diabetes mellitus composite events in MAGPIE

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system’s scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system’s ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.Peer ReviewedPostprint (author's final draft