Brain Tumor Classification From Magnetic Resonance Imaging Using Deep Learning and Novel Data Augmentation

Abstract

The complex and time-consuming nature of magnetic resonance imaging (MRI) may make it difficult to autonomously diagnose tumors in the brain, possibly leading to erroneous detection and classification. Identifying brain tumors is a complex process due primarily to relying on multiple modules for a comprehensive evaluation. In response, advances in deep learning have paved the way for automated medical image analysis and diagnostics. Convolutional neural networks (CNNs) are crucial for visual learning and image classification. The current investigation presents a novel approach for data augmentation that is integrated with state-of-the-art models, namely Efficient-NetB0, VGG16, ResNet50, InceptionV3, and MobileNetV2, to accurately classify various types of brain tumors, including glioma, meningioma, and pituitary tumors. The algorithm was subjected to testing utilizing benchmark data from existing literature.