TY - GEN
T1 - Analysis of Brain Tumor Progression Methods Used in Deep Learning-Based Brain MRI Tumor Diagnosis
AU - Memon, Qurban A.
AU - Almansoori, Aziza
AU - Alyaqoubi, Aisha
AU - Alkhatheri, Fatima
AU - Alshamsi, Aryam
AU - Alseiari, Amani
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Brain tumors present a major challenge in medical research due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) remains the leading non-invasive technique for analyzing these tumors. In recent years, significant advancements in deep learning models and the increasing availability of extensive datasets have driven remarkable progress in brain tumor progression analysis using MRI data. This article explores state-of-the-art deep learning models applied to MRIbased brain tumor prognosis, examining experimental findings and the technical challenges. Additionally, it investigates prognosis-related datasets, efforts in regulatory frameworks, and inconsistencies in benchmarking standards.
AB - Brain tumors present a major challenge in medical research due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) remains the leading non-invasive technique for analyzing these tumors. In recent years, significant advancements in deep learning models and the increasing availability of extensive datasets have driven remarkable progress in brain tumor progression analysis using MRI data. This article explores state-of-the-art deep learning models applied to MRIbased brain tumor prognosis, examining experimental findings and the technical challenges. Additionally, it investigates prognosis-related datasets, efforts in regulatory frameworks, and inconsistencies in benchmarking standards.
KW - Brain tumor datasets
KW - Brain tumor diagnosis and prognosis
KW - Deep learning
UR - https://www.scopus.com/pages/publications/105013846276
UR - https://www.scopus.com/pages/publications/105013846276#tab=citedBy
U2 - 10.1109/ICCMSO67468.2025.00062
DO - 10.1109/ICCMSO67468.2025.00062
M3 - Conference contribution
AN - SCOPUS:105013846276
T3 - Proceedings - 2025 4th International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2025
SP - 308
EP - 313
BT - Proceedings - 2025 4th International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2025
Y2 - 20 June 2025 through 22 June 2025
ER -