Revolutionizing Brain Tumor Diagnosis: leveraging Transfer Learning and Deep Neural Networks

Early detection of brain tumors helps to save a patient’s life to a greater extent. Artificial intelligence-driven deep learning techniques, already achieved remarkable accuracy in diagnosing brain tumors, with a trained large dataset of magnetic resonance imaging (MRI), which is the gold standard for brain tumor diagnosis. However, the complicated structure of the human brain forms significant challenges in this process. This study explores the potential of deep transfer learning architectures to enhance the precision of brain tumor diagnosis. The advanced transfer learning architectures such as MobileNetv3, DenseNet169, VGG19, and ResNet152—were meticulously evaluated using a Kaggle dataset, employing fivefold cross-validation for robust results. To address dataset imbalances, image enhancement techniques were applied, ensuring equal representation across four categories: pituitary tumors, normal scans, meningiomas, and gliomas. Among the models, DenseNet169 arose as the top performer, achieving an impressive accuracy of 99.75%, beating the others. These findings give priority of the groundbreaking potential of deep transfer learning in revolutionizing brain tumor diagnosis, offering hope for more accurate and efficient medical imaging solutions.