Automated Brain Tumor Classification and Detection Using Modified Convolutional Neural Networks for Early Diagnosis

In this project, we leverage cutting-edge deep learning techniques, including CNN, AlexNet, ResNets, and VGG-16, to classify MRI brain images. Our primary goal is early brain tumor detection, addressing the challenge of asymptomatic or vague symptoms. We employ a robust preprocessing pipeline for accurate brain region isolation, incorporating grayscale conversion, Gaussian blurring, thresholding, and artifact removal. Advanced image enhancement techniques like edge-based segmentation and clustering are applied to improve MRI analysis. Our deep learning-based system automates tumor diagnosis and segmentation, easing the burden on radiologists. With more than 2750 MRI images, we use data augmentation to tackle class imbalance, achieving an impressive 98% test accuracy. Beyond CNN, we explore alternative architectures and transfer learning to find the most effective model for MRI classification, revolutionizing early brain tumor detection and aiding medical professionals. This research has the potential to save lives and streamline the diagnostic process significantly.