Brain Tumor Classification Using CNN and Grad-CAM on MRI Images

Background: Proper and interpretable brain tumor classification is essential in making a successful clinical decision in neurooncology. Automated approaches are potentially promising, but a lack of transparency in decision-making is usually an obstacle to clinical implementation. Objective: The proposed research developed and evaluated a convolutional neural network (CNN) model in the context of automatic brain tumor classification using magnetic resonance images (MRI) with a particular focus on a high-performing model and visualizing predictions using Gradient-weighted Class Activation Mapping (Grad-CAM). Methods: It utilized a dataset of 7,023 MRI scans as a sample, which was divided into glioma, meningioma, pituitary tumors, and no-tumor. Preprocessing of the data was done by normalizing and resizing, and stratifying into training, validation, and test subsets. The suggested CNN has been compared with the state-of-the-art transfer-learning architectures, such as VGG16, MobileNetV2, and DenseNet121. Results: The proposed CNN had the highest predictive accuracy of 94.75%, precision of 94.99%, recall of 94.75%, and an F1-score of 94.82%, and better than all the transfer-learning baselines. Moreover, Grad-CAM visualizations have always identified tumor-specific areas in the images, confirming the clinical plausibility of the model decisions. Conclusions: These results highlight the possibility of high-performance CNN-based classification used in conjunction with explainable AI to provide effective and high-quality diagnostic support that is accurate, dependable, and explainable by clinicians. The future research will explore the concept of multi-modal MRI integration, 3D architecture, and privacy-preserving deployment schemes in the context of real-life healthcare applications.