Title: Deep learning approach for classifying ischemic stroke using DWI sequences of brain MRIs

Authors: Sukanta Sabut; Prasanta Patra; Arun Ray

Addresses: School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India ' School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India ' School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

Abstract: Stroke is an emergency condition and must be treated immediately to increase the survivability rate. We propose an automatic detection approach to identify the ischemic stroke infarcts based on deep neural network (DNN) architecture using diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging (MRI) images of the brain. A total 192 stroke images were collected at Kalinga Institute of Medical Science, India. Initially, all the images were pre-processed to reduce the noise and then taken for segmentation of infarct using a Delaunay triangulation (DT) approach. Thirty-four important features are extracted from the segmented infarct lesions and then classified with the DNN classifier. We achieved high detection rate with sensitivity 89.18%, specificity 95.37, Jaccard index 81.46% and accuracy of 92.8% in classifying the ischemic stroke into three sub-types. It is observed from compared results that the deep learning is an effective way to detect the stroke infarcts.

Keywords: computer-aided detection; image segmentation; Delaunay triangulation; classification; deep neural network; DNN.

DOI: 10.1504/IJISTA.2022.128526

International Journal of Intelligent Systems Technologies and Applications, 2022 Vol.20 No.6, pp.524 - 535

Received: 20 Sep 2021
Received in revised form: 22 Jul 2022
Accepted: 25 Aug 2022
Published online: 25 Jan 2023 *

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