Brain Tumor Detection Based on MRI Images and Artificial Intelligence

Brain tumors, as central nervous system diseases that endanger human health , require early and accurate detection to significantly improve patient survival rates. Magnetic Resonance Imaging (MRI), due to its superior soft-tissue contrast and non-invasiveness, has become a crucial tool in brain tumor diagnosis. However, traditional imaging diagnosis, which heavily relies on manual interpretation, suffers from limitations such as strong subjectivity and low efficiency. To address these issues, this paper proposes an automatic brain tumor detection method based on Convolutional Neural Networks (CNN). By leveraging deep learning techniques, the method extracts multi-level features from MRI images to achieve high-precision classification of glioma, meningioma, pituitary tumor, and non-tumor categories. A lightweight CNN model was developed, incorporating data augmentation and normalization preprocessing strategies. Experiments were conducted on a dataset of 7,023 MRI images. The results show that the model achieved classification accuracies of 96% on the training set and 95% on the validation set, demonstrating strong robustness and generalization capability. Confusion matrix analysis indicates that the model maintains high recognition accuracy across all categories, with particularly outstanding performance in identifying non-tumor and pituitary tumor cases. This study provides an effective technical pathway for intelligent assisted diagnosis of brain tumors and holds promising clinical application potential.