TY - EJOU AU - Omarov, Bakhytzhan AU - Kenjayeva, Balnur AU - Sultan, Daniyar AU - Ikram, Zhanseri TI - Oracle-Conditioned Deep Q-Network Refinement of Small Brain Tumor Components in Axial Magnetic Resonance Imaging T2 - Computer Modeling in Engineering \& Sciences PY - 2026 VL - 148 IS - 3 SN - 1526-1506 AB - 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 z-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. KW - Oracle-conditioned localization; lesion-wise ROI refinement; deep reinforcement learning; deep Q network (DQN); small brain tumor components; magnetic resonance imaging; medical image analysis DO - 10.32604/cmes.2026.088762