Development of a fast and accurate method for the segmentation of diabetic foot ulcer images

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyGlobally, Diabetic Foot Ulcers (DFUs) are among the major sources of morbidity and death among people diagnosed with diabetes. Diabetic foot ulcers are the leading diabetes-related complications that result in non-traumatic lower-limb amputations among these patients. Being a serious health concern, DFUs present a significant therapeutic challenge to specialists, particularly in countries with limited health resources and where the vast majority of patients are admitted to healthcare facilities when the ulcers have fully advanced. Clinical practices currently employed to assess and treat DFU are mostly based on the vigilance of both the patient and clinician. These practices have been proved to experience major limitations which include less accurate assessment methods, time-consuming diagnostic procedures, and relatively high treatment costs. Digital image processing is thus a potential solution to address issues of the inaccuracy of visual assessment as well as minimizing consecutive patient visits to the clinics. Image processing techniques for ulcer assessment have thus been a center of study in various works of literature. In the available works of literature, these methods include measuring the ulcer area as well as using a medical digital photography scheme. The most notable drawbacks of such approaches include system complexity, complex-exhaustive training phases, and high computational cost. Inspired by the weaknesses of the existing techniques, this study proposes a segmentation method that incorporates a hybrid diffusion-steered functional derived from the Total variation and the Perona-Malik diffusivities, which have been reported that they can effectively capture semantic features in images. Empirical results from the experiments that were carried out in the MATLAB environment show that the proposed method generates clearer segmented outputs with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks

Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity

Wound Image Classification Using Deep Convolutional Neural Networks

Artificial Intelligence (AI) includes subfields like Machine Learning (ML) and DeepLearning (DL) and discusses intelligent systems that mimic human behaviors. ML has been used in a wide range of fields. Particularly in the healthcare domain, medical images often need to be carefully processed via such operations as classification and segmentation. Unlike traditional ML methods, DL algorithms are based on deep neural networks that are trained on a large amount of labeled data to extract features without human intervention. DL algorithms have become popular and powerful in classifying and segmenting medical images in recent years. In this thesis, we shall study the image classification problem in smartphone wound images using deep learning. Specifically, we apply deep convolutional neural networks (DCNN) on wound images to classify them into multiple types including diabetic, pressure, venous, and surgical. Also, we use DCNNs for wound tissue classification. First, an extensive review of existing DL-based methods in wound image classification is conducted and comprehensive taxonomies are provided for the reviewed studies. Then, we use a DCNN for binary and 3-class classification of burn wound images. The accuracy was considerably improved for the binary case in comparison with previous work in the literature. In addition, we propose an ensemble DCNN-based classifier for image-wise wound classification. We train and test our model on a new valuable set of wound images from different types that are kindly shared by the AZH Wound and Vascular Center in Milwaukee. The dataset has been shared for researchers in the field. Our proposed classifier outperforms the common DCNNs in classification accuracy on our own dataset. Also, it was evaluated on a public wound image dataset. The results showed that the proposed method can be used for wound image classification tasks or other similar applications. Finally, experiments are conducted on a dataset including different tissue types such as slough, granulation, callous, etc., annotated by the wound specialists from AZH Center to classify the wound pixels into different classes. The preliminary results of tissue classification experiments using DCNNs along with the future directions have been provided

Human Face Mapping Based on TEWL, Hydration and Ultrasound

Biophysical properties of the skin vary depending on the skin location. Such properties include skin structure, density of skin layers, pH, temperature, hydration and Transepidermal Water Loss (TEWL).Modern technologies and quantitative methods allow reading and analysing the skin properties using in-vivo based analysis. One goal of such analysis is partitioning the skin in areas with similar properties, which is referred as mapping. The purpose of our study, also the novelty of the project, is mapping of the facial skin in terms of TEWL, hydration and skin layer thickness, as well as measuring the effect of physical exercise on facial skin; where possible, effect of sex and age were also considered. TEWL was measured with AquaFlux, skin layer thickness was measured with Episcan high resolution ultrasound imaging, and skin hydration was measured with Epsilon. Our study reveals material difference of TEWL between the facial sites being analysed; the largest differences were noted between the lips and the neck. It was found that skin hydration levels decrease with the advancement of age. Skin hydration readings reveal larger general effect of exercise for females, and strongest effect for males observed on the nose. Skin ultrasound images were used in two ways. First, face was mapped in terms of the thickness of the individual skin layers and such mapping was found to be different for each layer. Secondly, the differences between the sites in terms of thickness were quantified using Welch test, where age was also found to be a factor. Several Machine Learning-based classifiers of the skin location were also trained, which are based on the cross-sectional image with moderate positive outcome. The study showed that the combination of TEWL, Epsilon and Episcan provides useful information about skin health. The study also showed variations in the values for different facial skin sites of several skin samples, which was likely due to the degree of corneocyte formation, the lipid contents of the Stratum Corneum (SC), skin temperature, damaged barrier function, bodily health and skin blood flow

A systematic review of objective burn scar measurements

BackgroundProblematic scarring remains a challenging aspect to address in the treatment of burns and can significantly affect the quality of life of the burn survivor. At present, there are few treatments available in the clinic to control adverse scarring, but experimental pharmacological anti-scarring strategies are now beginning to emerge. Their comparative success must be based on objective measurements of scarring, yet currently the clinical assessment of scars is not carried out systematically and is mostly based on subjective review of patients. However, several techniques and devices are being introduced that allow objective analysis of the burn scar. The aim of this article is to evaluate various objective measurement tools currently available and recommend a useful panel that is suitable for use in clinical trials of anti-scarring therapies.MethodsA systematic literature search was done using the Web of Science, PubMed and Cochrane databases. The identified devices were then classified and grouped according to the parameters they measured.The tools were then compared and assessed in terms of inter- and intra-rater reproducibility, ease of use and cost.ResultsAfter duplicates were removed, 5062 articles were obtained in the search. After further screening, 157 articles which utilised objective burn scar measurement systems or tools were obtained. The scar measurement devices can be broadly classified into those measuring colour, metric variables, texture, biomechanical properties and pathophysiological disturbances.ConclusionsObjective scar measurement tools allow the accurate and reproducible evaluation of scars, which is important for both clinical and scientific use. However, studies to evaluate their relative performance and merits of these tools are scarce, and there remain factors, such as itch and pain, which cannot be measured objectively. On reviewing the available evidence, a panel of devices for objective scar measurement is recommended consisting of the 3D cameras (Eykona/Lifeviz/Vectra H1) for surface area and volume, DSM II colorimeter for colour, Dermascan high-frequency ultrasound for scar thickness and Cutometer for skin elasticity and pliability