Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labor-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.

翻译：通过磁共振成像（MRI）早期检测脑肿瘤对于及时治疗至关重要，然而偏远地区对诊断设施的获取仍然有限。胶质瘤作为最常见的原发性脑肿瘤，源于大脑和脊髓中胶质细胞的癌变过程，其中胶质母细胞瘤患者的中位生存期不足14个月。MRI作为一种无创且有效的肿瘤检测手段，但脑部MRI扫描的手动分割历来是神经放射科医生劳动密集型的任务。计算机辅助设计（CAD）、机器学习（ML）与深度学习（DL）领域的最新进展为自动化此过程提供了可行方案。本研究提出一种基于MRI数据的自动化深度学习模型用于脑肿瘤检测与分类。该模型通过融入空间注意力机制，实现了96.90%的准确率，增强了上下文信息聚合以提升模式识别能力。实验结果表明，所提方法在性能上超越基线模型，彰显了其在推进基于MRI的自动化脑肿瘤分析方面的鲁棒性与应用潜力。