This study proposes a hybrid ensemble deep learning model for ischemic brain stroke detection and classification using brain computed tomography (CT) images. The proposed approach integrates image enhancement techniques, ensemble deep learning algorithms, and intelligent lesion detection and segmentation models to improve diagnostic performance. The model was trained and tested using a dataset of 10,000 CT scans, demonstrating significant improvements in stroke detection accuracy across different stages. The findings highlight the potential of the proposed model as a computer-aided diagnosis system to support clinical decision-making.

Title

Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

Publisher Country

Switzerland

Indexing

Scopus

Impact Factor

3.2

Publication Type

Online only

Volume

10

Year

2024

Pages

J. Imaging 2024, 10(7), 160