Brain MRI Classification Using Deep Learning Algorithm

Deep learning methods have shown transformative capacity in the basic problem of MRI scan-based brain tumor classification in medical diagnosis. This paper proposes a full framework to categorize brain tumors into several groups using Convolutional Neural Networks (CNNs) and a hybrid CNN-Support Vector Machine (CNN-SVM) method. The work focuses on robust data preparation including augmentation and normalization using BRATS and Sartaj datasets and effectively address the variability in MRI images. Carefully trained and evaluated, the proposed CNN and CNN-SVM models clearly improve classification accuracy; CNN-SVM exhibits greater accuracy for difficult situations. The work also includes in important investigation using hyper parameter tuning, optimization methodologies, and model validation in order to ensure dependability and clinical relevance dependability and clinical relevance. Among other performance criteria, accuracy, precision, recall, and F1 score let models be compared to current approaches. Results confirm the performance of the hybrid CNN-SVM over conventional CNNs especially in noisy or restricted data settings. The paper proposes solutions for overfitting, computational resource constraints, data imbalance, and other issues as well as for This work underlines how precisely and early diagnosis might enable deep learning models to enhance patient outcomes, hence strengthening brain MRI classification.