Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System

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Roohi Majid; Mazloum Jalil; POURMINA MOHAMMAD ALI; Ghalamkari Behbod

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Journal Paper

Paper Information

Journal: Journal of Intelligent Procedures in Electrical Technology Year:2024 | Volume:15 | Issue:57 Page(s): 121-132

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Title

Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System

Author(s)

Roohi Majid | Mazloum Jalil | POURMINA MOHAMMAD ALI | Ghalamkari Behbod | Issue Writer Certificate

Keywords

confocal image reconstruction algorithm

convolutional neural network

support vector machine classifier

intracranial hemorrhage stroke detection

microwave head imaging system

Abstract

One of the main reasons of death in the world, mostly affecting seniors, is brain stroke. Almost 85% of all brain strokes are ischemic due to internal bleeding in a part of the brain. Due the high mortality rate, quick diagnosic and treatment of ischemic and hemorrhagic strokes are of utmost importance. In this paper, to realize microwave brain imaging system, a circular array-based of modified bowtie antennas located around the multilayer head phantom with a spherical target with radius of 1 cm as intracranial hemorrhage target aresimulated in CST simulator. To obtain satisfied radiation characteristics in the desired band (from 0. 5-5 GHz) an appropriate matching medium is designed. First, in the processing section, a confocal image-reconstructing method based using delay and sum (DAS) and delay, multiply and sum (DMAS) beam-forming algorithms is used. The reconstructed images generated shows the usefulness of the proposed confocal method in detecting the spherical target in the range of 1 cm. The main purpose of this paper is stroke classification using deep learning approaches. For this, an image classification algorithm is developed to estimate the stroke type from reconstructed images. By using the proposed deep learning method, the reconstructed images are classified into different categories of cerebrovascular diseases using a multiclass linear support vector machine (SVM) trained with convol-utional neural networks (CNN) features extracted from the images. The simulated results show the suitability of the proposed image reconstruction method for precisely localizing bleeding targets, with 89% accuracy in 9 seconds. In addition, the proposed deep-learning approach shows good performance in terms of classification, since the system does not confuse between different classes.

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APA: Copy

Roohi, Majid, Mazloum, Jalil, POURMINA, MOHAMMAD ALI, & Ghalamkari, Behbod. (2024). Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, 15(57 ), 121-132. SID. https://sid.ir/paper/1034948/en

Vancouver: Copy

Roohi Majid, Mazloum Jalil, POURMINA MOHAMMAD ALI, Ghalamkari Behbod. Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY[Internet]. 2024;15(57 ):121-132. Available from: https://sid.ir/paper/1034948/en

IEEE: Copy

Majid Roohi, Jalil Mazloum, MOHAMMAD ALI POURMINA, and Behbod Ghalamkari, “Brain Stroke Classification Based on Deep Learning Approach in Microwave Brain Imaging System,” JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, vol. 15, no. 57 , pp. 121–132, 2024, [Online]. Available: https://sid.ir/paper/1034948/en

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