What question did this study set out to answer?

To develop and evaluate a deep learning framework for automated brain stroke classification from CT images.

Deep ensemble learning with convolutional and transformer models for brain stroke classification

View Full Paper

Ask AI

Bookmark

Authors

Hybrid model classifies brain stroke from CT images, indicating effective automated assessment for stroke diagnosis.

To develop and evaluate a deep learning framework for automated brain stroke classification from CT images.
Combined convolutional neural network (CNN) and vision transformer (ViT) for feature extraction.
Utilized a lightweight feed-forward network for feature fusion.
Evaluated the model on a publicly available brain CT dataset with a fixed train-validation-test split.
Achieved 99.77% accuracy and 99.24% precision in classification.
Demonstrated 100% recall and an F1-score of 99.62%.
Confusion matrix showed zero false negatives and only one false positive.

Turyamuhaki et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark