Deep CNN-Based Numerical Modeling for Optimizing Mathematical Models in Brain Image Segmentation and Classification

This research investigates the application of convolutional neural networks (CNNs) for the segmentation and classification of brain tumors using the Brain Tumor Segmentation (BRATS) dataset, a comprehensive collection of annotated MRI scans. The objective was to develop and optimize a CNN model capable of accurately identifying and delineating various tumor regions within the brain to support clinical decision-making and treatment planning. The methodology involved preprocessing the MRI data for standardization and enhancement through normalization and augmentation techniques such as rotation, scaling, and elastic deformation. This was followed by designing a CNN architecture that includes convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. The model's training was guided by a combination of dice coefficient and cross-entropy loss functions, with hyperparameters finely tuned through methods such as grid and random search to achieve optimal performance. The resulting model demonstrated high accuracy, achieving an overall accuracy of 93.5% with a Dice coefficient of 89.7%. Class-specific performance metrics indicated particularly effective segmentation of the whole tumor region. Data augmentation significantly improved the model's robustness and ability to generalize across unseen data, a crucial aspect for clinical applications. The findings underscore the potential of advanced deep learning techniques in medical imaging, particularly in enhancing the precision and efficiency of brain tumor diagnostics. This research paves the way for further exploration into real-time applications and integration into clinical workflows, highlighting the importance of aligning AI developments with clinical needs. Future work will focus on refining the model's architecture and exploring its applicability to other imaging modalities and tumor types.