Automatic Classification of Foot Thermograms Using Machine Learning Techniques

Improving Physical, Physiological, and Psychological Health Outcomes in Patients with Diabetic Foot Ulcers - State of the Art

Diabetic foot disease is a complex and challenging complication of diabetes mellitus, which imposes a significant burden of disease on patients, their carers, and the wider health systems. Recurrence rates are high, and current evidence indicates a high mortality associated with it. While management algorithms have primarily focused on the physical aspects of healing, there is increasing recognition of the critical role played by psychological and biomechanical factors in the development and resolution of diabetic foot disease. Therefore, in this paper, we aim to explore how diabetic foot outcomes can be improved by addressing not only the physical but also the psychological and biomechanical aspects that are integral to the development of this condition and its optimal resolution. We explore new technologies that allow for non-invasive objective assessment of the diabetic foot at risk, and we also explore the role of understanding biomechanics, which is essential to determining risk of foot disease, but also the potential for recurrence. In addition, we discuss the evidence linking depression and cognitive impairment to diabetic foot disease and offer our insight on the research direction required before implementing novel information into front-line clinics.

Grading of gliomas using transfer learning on MRI images

OBJECTIVE

Despite the critical role of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumours, there are still many pitfalls in the exact grading of them, in particular, gliomas. In this regard, it was aimed to examine the potential of Transfer Learning (TL) and Machine Learning (ML) algorithms in the accurate grading of gliomas on MRI images.

MATERIALS AND METHODS

Dataset has included four types of axial MRI images of glioma brain tumours with grades I-IV: T1-weighted, T2-weighted, FLAIR, and T1-weighted Contrast-Enhanced (T1-CE). Images were resized, normalized, and randomly split into training, validation, and test sets. ImageNet pre-trained Convolutional Neural Networks (CNNs) were utilized for feature extraction and classification, using Adam and SGD optimizers. Logistic Regression (LR) and Support Vector Machine (SVM) methods were also implemented for classification instead of Fully Connected (FC) layers taking advantage of features extracted by each CNN.

RESULTS

Evaluation metrics were computed to find the model with the best performance, and the highest overall accuracy of 99.38% was achieved for the model containing an SVM classifier and features extracted by pre-trained VGG-16.

DISCUSSION

It was demonstrated that developing Computer-aided Diagnosis (CAD) systems using pre-trained CNNs and classification algorithms is a functional approach to automatically specify the grade of glioma brain tumours in MRI images. Using these models is an excellent alternative to invasive methods and helps doctors diagnose more accurately before treatment.

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.