Advanced Hybrid Method for Precise Identification and Categorization of Brain Stroke from CT Images
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 10 |
| Year of Publication : 2024 |
| Authors : Shahina A.R, I. Sowmy |
10.14445/23488549/IJECE-V11I10P111
How to Cite?
Shahina A.R, I. Sowmy, "Advanced Hybrid Method for Precise Identification and Categorization of Brain Stroke from CT Images," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 10, pp. 135-148, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I10P111
Abstract:
Brain stroke is a serious medical condition that occurs when the brain’s blood supply is disrupted, either due to a blockage or the rupture of a blood vessel. This interruption results in a sudden loss of brain function, manifesting as symptoms like difficulty speaking, weakness or paralysis of the limbs, confusion, or altered consciousness. The severity of a stroke is influenced by both the duration of the blood flow disruption and the specific location of the damage within the brain. Immediate medical intervention is crucial for reducing damage and improving the chances of recovery. Key risk factors for stroke include hypertension, diabetes, obesity, a sedentary lifestyle, and smoking. Given the critical need for timely and accurate stroke diagnosis, this study introduces a novel Deep Learning (DL) model for detecting and classifying brain strokes using brain CT images. The proposed method combines DenseNet 201 and Capsule Network (CapsNet) models to enhance classification accuracy. Experimental results demonstrate that the model achieved an accuracy of 93.45%, a precision of 92.18%, a recall of 92.56%, and an F1 score of 92.36%, underscoring its effectiveness in diagnosing and classifying strokes with high accuracy.
Keywords:
Brain Stroke, DenseNet 201, Capsule network, CT images, Medical imaging, Deep learning.
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