Advancements in deep learning algorithms for medical imaging, combined with the integration of cyberworlds, have shown great promise in providing precise diagnostic results. One area of interest is the application of these advancements in enhancing personalized treatment of gliomas, a particularly challenging type of brain tumor, by providing more reliable information on a clinical biomarker Oxygen 6-methylguanine-DNA methyltransferas (MGMT). To achieve accurate results, a MobileNetV2 model was employed, utilizing transfer learning method and a mechanism spatial attention with correlation was added to further enhance the model's performance. The model was trained using a private dataset of annotated images and evaluated using cross-validation. Results showed high precision and recall in predicting MGMT status, indicating its potential to improve the efficiency of this prediction. The model's ability to predict MGMT promoter methylation status can help clinicians make more informed decisions, which has the potential to improve personalized treatment planning, ultimately leading to better patient outcomes.