Volume 15, Issue 4 · IJARST · ISSN 2319-1783 / 2320-1126

Stroke Sense: Deep Learning for Early Stroke Detection via Brain Scans

Stroke Sense: Deep Learning for Early Stroke Detection via Brain Scans is an open-access, peer-reviewed research paper by Mrs S. Lalitha, Aranganathan R, Aslam basha I, Balaganeshan A, Dinesh Kumar C, published in Volume 15, Issue 4 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Mrs S. Lalitha, Aranganathan R, Aslam basha I, Balaganeshan A, Dinesh Kumar C

Abstract

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.

DOI

https://doi.org/10.62226/ijarst20262656

PAGES : 1901-1904 | 10 VIEWS | 5 DOWNLOADS

How do you cite this paper?

Mrs S. Lalitha, Aranganathan R, Aslam basha I, Balaganeshan A, Dinesh Kumar C — “Stroke Sense: Deep Learning for Early Stroke Detection via Brain Scans.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 4. DOI: https://doi.org/10.62226/ijarst20262656.

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