TY - GEN
T1 - Lightweight Brain Tumor Diagnosis via Knowledge Distillation
AU - Anantathanavit, Rungpilin
AU - Raswa, Farchan Hakim
AU - Thaipisutikul, Tipajin
AU - Wang, Jia Ching
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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
AB - 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
KW - Brain tumor
KW - Knowledge distillation
KW - deep learning
KW - medical
UR - http://www.scopus.com/inward/record.url?scp=85204801636&partnerID=8YFLogxK
U2 - 10.1109/MAPR63514.2024.10660863
DO - 10.1109/MAPR63514.2024.10660863
M3 - 會議論文篇章
AN - SCOPUS:85204801636
T3 - 2024 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 - Proceedings
BT - 2024 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024
Y2 - 15 August 2024 through 16 August 2024
ER -