Automated Brain Tumor segmentation using hybrid YOLO and SAM

Our suggested model achieves good performance of Precision as 94.2%, Recall as 95.6% and mAP50(B) score as 96.5% demonstrating and highlighting the effectiveness of the proposed approach for early-stage brain tumor diagnosis Conclusion: The validation is demonstrated through a comprehensive ablation study. The robustness of the system makes it more suitable for clinical deployment.

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Publication Details

Journal
Current Medical Imaging Formerly Current Medical Imaging Reviews
Published
2025-08-04
DOI
https://doi.org/10.2174/0115734056392711250718201911
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

Automated Brain Tumor segmentation using hybrid YOLO and SAM

Senthil Kumar Madasamy
Current Medical Imaging Formerly Current Medical Imaging Reviews
Brain Tumor Detection and Classification
article

Automated Brain Tumor segmentation using hybrid YOLO and SAM

Senthil Kumar Madasamy
article en

Abstract

Our suggested model achieves good performance of Precision as 94.2%, Recall as 95.6% and mAP50(B) score as 96.5% demonstrating and highlighting the effectiveness of the proposed approach for early-stage brain tumor diagnosis Conclusion: The validation is demonstrated through a comprehensive ablation study. The robustness of the system makes it more suitable for clinical deployment.

Current Medical Imaging Formerly Current Medical Imaging ReviewsVol. 21
Department of Biotechnology (IN)
Good health and well-being
Openalex Percentile: Top 76%
Brain Tumor Detection and Classification
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