Brain Tumor Localization and Multimodal Segmentation in MRI Images by Automated Process

In neuro-oncology, precise MRI tumor segmentation and localization are essential for efficient diagnosis and therapy planning. Although automated segmentation methods have advanced, there are still difficulties in reaching high efficiency and accuracy. A new ML framework for automated multimodal brain tumor localization and segmentation in MRI imaging is proposed. The model incorporates various MRI modalities to improve segmentation accuracy and localization precision by using CNNs and DL architectures. The quality of MRI datasets is standardized and enhanced by data preprocessing approaches, and model training optimizes validation measures and loss functions to assess performance. The suggested model yields significant gains in tumor segmentation accuracy and localization precision compared to earlier methods. Evaluation measures show that the model performs better than earlier approaches on various MRI datasets, including Sensitivity, Specificity, and Dice Similarity Coefficient. The results highlight the effectiveness of the suggested machine learning framework in automating the segmentation and localization of brain tumors, providing a dependable tool to support physicians in the practice of neuro-oncology.