Employing CNN Deep Learning for the Detection and Classification of Multiple Types of Brain Tumors

Brain tumors, a leading cause of mortality, necessitate accurate and timely detection for effective treatment. Recent advancements in deep learning and medical imaging have significantly enhanced the capability to identify and classify cancers. This extensive study focuses on three specific tumor types: glioma, meningioma, and pituitary tumors and explores the application of deep transfer learning methods for brain tumor classification utilizing convolutional neural networks (CNN). By analyzing MRI scans, the research assesses the performance of pretrained CNN models, including ResNet-50, Inception-v3, and VGG-16 in automating tumor prediction. The study employs performance metrics such as accuracy, precision, recall, and F1 scores to evaluate how effectively each model aids in early diagnosis and improves clinical decision-making.