Classification of brain strokes using divergence-based convolutional neural networks

Abstract

Stroke is a cerebrovascular issue caused by reduced or interrupted blood flow to the brain, resulting in the rapid death of brain cells and potential permanent functional damage. Early detection, classification, and immediate intervention are essential to prevent severe consequences, including death or lifelong disabilities. In this study, a total of 6650 brain CT images were used, which included 1130 ischemic stroke, 1093 hemorrhagic stroke, and 4427 non-stroke cases provided by the Turkish Ministry of Health. The study first aimed to determine the presence of stroke in the brain. Secondly, in cases where stroke was detected, the type of stroke, whether ischemic or hemorrhagic, was determined. Lastly, the images were classified into three categories: non-stroke, hemorrhagic, and ischemic. A newly introduced divergence-based deep neural network (DNN) was modified and utilized for the method. Features were extracted from the convolutional neural network (CNN) using Walsh matrices, and classification was performed using the minimum distance network (MDN). Experimental results showed that when the images were binary classified as stroke vs. non-stroke and ischemic vs. hemorrhagic type, they achieved accuracies of 99.248% and 99.324%, respectively. For the three-class classification (non-stroke, ischemic, and hemorrhagic), a success rate of 99.097% was achieved. The proposed study has advantages in terms of no preprocessing stage, classification with real images without data augmentation, and the low number of parameters in the employed network. The results indicate that the proposed network successfully detects the presence and type of stroke with high accuracy and outperforms existing studies in the literature.