Mrs S. Lalitha, Aranganathan R, Aslam basha I, Balaganeshan A, Dinesh Kumar C
Brain stroke is one of the leading causes of death and long-term disability worldwide. Early detection of stroke through brain imaging can significantly improve patient survival and recovery rates. In this paper, we propose a deep learning-based stroke detection system called Brain Stroke Sense, which analyzes brain CT/MRI scans to automatically detect the presence of stroke at an early stage. The system uses Convolutional Neural Networks (CNN) for feature extraction and classification of stroke types. The trained model classifies scans into Normal, Ischemic Stroke, or Hemorrhagic Stroke categories. The proposed system aims to assist doctors by providing fast and accurate predictions. Experimental results show high accuracy and reliability, making it suitable for real-time medical support systems.
https://doi.org/10.62226/ijarst20262656
PAGES : 1901-1904 | 10 VIEWS | 5 DOWNLOADS
Mrs S. Lalitha, Aranganathan R, Aslam basha I, Balaganeshan A, Dinesh Kumar C | Stroke Sense: Deep Learning for Early Stroke Detection via Brain Scans | DOI : https://doi.org/10.62226/ijarst20262656
| Journal Frequency: | ISSN 2320-1126, Monthly | |
| Paper Submission: | Throughout the month | |
| Acceptance Notification: | Within 6 days | |
| Subject Areas: | Engineering, Science & Technology | |
| Publishing Model: | Open Access | |
| Publication Fee: | USD 60 USD 50 | |
| Publication Impact Factor: | 6.76 | |
| Certificate Delivery: | Digital |