Brain Stroke Classification Using YOLOv8
Bibliographic record
Abstract
Brain stroke is a leading cause of mortality and long-term disability worldwide, underscoring the critical importance of rapid and accurate diagnosis for effective clinical intervention. This paper presents an automated classification approach for identifying stroke types, specifically ischemic stroke, hemorrhagic stroke, and normal brain tissues from computed tomography (CT) images using the YOLOv8 deep learning architecture. The study employs the Brain Stroke CT Dataset from the TEKNOFEST-2021 Artificial Intelligence in Healthcare Competition, which contains 6,653 anonymized and expertlabeled axial CT slices. The methodology employed in this study includes a comprehensive data preprocessing model, a fine-tuning stage using transfer learning, and performance evaluation using key metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that YOLOv8 achieves high classification accuracy across all the three categories mentioned above. This is supported by a detailed confusion matrix and visual examples of model predictions. These findings suggest that YOLOv8 can serve as a powerful tool for assisting clinicians in the rapid triage and diagnosis of patients with a brain stroke, particularly in time-sensitive scenarios. This work contributes to the growing application of artificial intelligence in medical image analysis for effective and timely decision making by offering a practical and efficient solution for stroke classification, with potential applications in both resource-limited and high-volume healthcare settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".