Automatic detection of ischemic-stroke-lesion with CNN segmentation: a study

The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN)...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-08, Vol.2318 (1), p.12049
Main Authors: Al Attar, FerasNadhimHasoon, Kadry, Seifedine, Manic, K. Suresh, Meqdad, Maytham N.
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Brain
Emergency medical services
Flair modality
Image segmentation
Magnetic resonance imaging
Medical imaging
Physics
Segmentation
Stroke
Stroke lesion
UNet
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Summary:The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN) segmentation method to extract and evaluate the infected portion from the MRI slice of the brain. In our study the pre-trained UNet scheme is adopted to extract the stroke region from the Flair modality MRI slice with axial-, coronal- and sagittal plane. In this work, the ISLES2015 database is used for the experimental investigation. The segmented portion is further evaluated to the ground-truth and the metrics such as Jaccard, Dice and Accuracy are computed. The experimental investigation is implemented using Python software. The experimental outcome of this research proves that the proposed CNN scheme aids to improve segmentation accuracy on axial-plane images compared with other images. The performance of the CNN segmentation scheme is then validated with other related results existing in the literature. The outcome of this study confirms that UNet supported technique helps extract the stroke lesion from the MRI slice with more accurate accuracy.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2318/1/012049