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