Automated Brain Tumor Classification with Deep Learning

Brain Tumors are the abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification. In this paper, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract features and classify brain MRI images. Specifically, Convolutional Neural Networks (CNNs) are trained on a diverse dataset of brain tumor images. The CNN learns intricate patterns and features within the images, enabling it to classify various tumor types. Transfer learning, utilizing pre-trained models such as Visual Geometry Group and EfficientNet, enhances the CNN model’s ability to generalize across different datasets. The performance of the Visual Geometry Group and EfficientNet models are evaluated and compared. The metrics like accuracy, precision, recall and F1 score are used to evaluate the efficacy of each model in brain tumor classification. This project contributes to the advancement of automated brain tumors diagnosis, potentially improving patient outcomes through more efficient diagnosis strategies.