@Article{cmes.2026.088762,
AUTHOR = {Bakhytzhan Omarov, Balnur Kenjayeva, Daniyar Sultan, Zhanseri Ikram},
TITLE = {Oracle-Conditioned Deep Q-Network Refinement of Small Brain Tumor Components in Axial Magnetic Resonance Imaging},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {3},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v148n3/69014},
ISSN = {1526-1506},
ABSTRACT = {Brain tumor localization in magnetic resonance imaging remains challenging for small, low-contrast lesions. This study presents Small Brain Lesion Deep Q Network (SBL-DQN), a two-dimensional lesion-wise reinforcement-learning framework that refines an axis-aligned region of interest on a co-registered axial multi-channel MRI slice. In the current evaluation protocol, voxel reference masks are resampled, processed by 26-connected-component analysis, and filtered using a maximum in-plane diameter of 10 mm; one axial episode is then executed for each reference-enumerated component. The mask and component coordinates are not included in the DQN state, but ground-truth annotations are required to generate the evaluation candidate list and to compute reward traces and localization metrics. Accordingly, the reported experiment is oracle-conditioned lesion-wise localization refinement rather than autonomous lesion detection. The agent uses seven actions (up, down, left, right, zoom in, zoom out, and stop) and does not move through the anatomical <i>z</i>-axis. On the internal BraTS 2025 evaluation, the complete configuration yielded 97.46% localization accuracy, 97.23% precision, 96.88% recall, 97.05% F1-score, mean Intersection over Union of 0.834, AP@0.5 of 0.961, and center localization error of 0.91 mm. BraTS-METS 2025 and QIN-GBM-TREATMENT-RESPONSE remain qualitative transfer examples. Component-wise ablation, eligible-lesion accounting, and a balanced failure-case archive were not retained in the available experimental record; therefore, no individual component contribution, autonomous detection performance, or comprehensive robustness claim is made.},
DOI = {10.32604/cmes.2026.088762}
}