Pattern Recognition of White Matter Lesions Associated With Diabetes Mellitus Type 2

The White Matter Hyperintensities (WMHs) are usually associated with diabetes which is relevant in medical research to understand the long-term affection of diabetes. However, there is not enough evidence to distinguish whether the WMHs observed in diabetes subjects are structurally different from those observed in healthy subjects. This work aims to recognize the patterns associated with diabetes using the WMHs features of diabetic patients. We used Machine Learning models, such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and a Multilayer perceptron (MLP) Neural Network to classify the features extracted from the WMH segments from T1 and FLAIR sequences of Magnetic Resonance Images (MRI) obtained from diabetic patients. Four classification models were evaluated and compared in their performance and Logistic Regression showed the best results, with an accuracy of 88%, as belonging or not to a diabetic class. Our results showed that diabetic patients have WMH patterns that are structurally different from controls, which may be useful for patients follow up.