Optimized Brain Tumor Diagnosis Through Convolutional Neural Networks

Brain tumors such as gliomas, meningiomas, and pituitary tumors are critical health issues with high mortality rates and necessitate early and precise diagnosis to facilitate adequate treatment. To improve brain tumor classification in MRI data, we propose a new Convolutional Neural Network (CNN) architecture. In contrast to ordinary methods, the proposed CNN incorporates advanced preprocessing techniques and regularization strategies to enhance feature extraction, decrease noise, and improve classification efficiency. The data consists of 7,023 MRI images, labeled as gliomas, meningiomas, pituitary tumors, and no tumor, of which 5,712 are used for training and 1,311 images are used for testing. With 98 % accuracy, the model outperforms traditional methods with strong sensitivity and recall and shows notable gains across major metrics like Precision, ROC, and F1 Score. A novelty of this work is that it integrates numerous state-of-the-art advances in preprocessing, regularization and architectural layers to establish new state-of-the-art methods for automated brain tumor classification and detection facilitating earlier diagnosis and treatment.