Towards effective classification of brain hemorrhagic and ischemic stroke using CNN

Brain stroke is one of the most leading causes of worldwide death and requires proper medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography (CT) scan images into hemorrhagic stroke, ischemic stroke and normal. Our newly proposed convolutional neural network (CNN) model utilizes image fusion and CNN approaches. Initially, some preprocessing operations have been employed by using multi-focus image fusion in order to improve the quality of CT images. Further, preprocessed images are fed into the newly proposed 13 layers CNN architecture for stroke classification. The robustness of our CNN method has been checked by conducting two experiments on two different datasets. In the first experiment, CT image dataset is partitioned into 20% testing and 80% training sets, while in the second experiment, 10 fold cross-validation of the image dataset has been performed. The classification accuracy obtained by our method on dataset 1 in the first experiment is 98.33% and in the second experiment, it is 98.77%, while in dataset 2 accuracy obtained in experiment 1 and 2 is 92.22% and 93.33% respectively. All the experiments have been conducted on the real CT image dataset which we have been collected from Himalayan Institute of Medical Sciences (HIMS), Dehradun, India. The results obtained by the proposed method have also been compared with AlexNet and ResNet50 where results show improvement over these CNN architectures.

Automated summary

This study aims to classify brain CT images using a newly proposed convolutional neural network, showing high accuracy in two experiments and improvement over traditional CNN architectures.