Lightweight Brain Tumor Diagnosis via Knowledge Distillation
Issued Date
2024-01-01
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Scopus ID
2-s2.0-85204801636
Journal Title
2024 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 - Proceedings
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SCOPUS
Bibliographic Citation
2024 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 - Proceedings (2024)
Suggested Citation
Anantathanavit R., Raswa F.H., Thaipisutikul T., Wang J.C. Lightweight Brain Tumor Diagnosis via Knowledge Distillation. 2024 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 - Proceedings (2024). doi:10.1109/MAPR63514.2024.10660863 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101440
Title
Lightweight Brain Tumor Diagnosis via Knowledge Distillation
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Abstract
Brain tumors pose a significant medical challenge, necessitating precise and rapid diagnosis for effective treatment and improved patient outcomes. This paper introduces knowledge distillation, which has the potential to revolutionize brain tumor diagnosis by enabling early identification from medical imaging data. Using a sophisticated teacher' model to capture intricate patterns, we distill this knowledge into a more efficient "student' model, aiming for comparable accuracy with reduced memory usage and improved inference times. Our method, based on a dataset of 357 MRI scans, demonstrated the potential of knowledge distillation in brain tumor diagnosis, offering a promising avenue for advancing patient care. The proposed model serves as a vital tool for healthcare practitioners, providing accurate and efficient support in detecting brain tumors and contributing to advancements in healthcare technology. The evaluation results indicate the effectiveness of our technique, achieving an impressive accuracy of 98.10