Leveraging Capsule Networks for Robust Brain Tumor Classification and Detection in MRI Scans

Sandeep Shiraskar, Simon Vellandurai, Dominick Rizk

Abstract: Brain tumors are life-threatening conditions where early detection and accurate classification are critical for timely and effective treatment. Misclassification or delayed identification of tumors can result in fatal consequences. Current deep learning techniques, predominantly based on Convolutional Neural Networks (CNNs), have demonstrated success in tumor detection but face limitations due to their inability to handle diverse and extensive datasets effectively. Moreover, CNNs suffer from information loss in pooling layers, leading to suboptimal performance in capturing global dependencies in MRI tumor images. To overcome these challenges, we propose the use of a modified Capsule Network to address the limitations of CNNs. Capsule Networks retain spatial hierarchies and dependencies, enabling improved performance in tumor detection and classification tasks. Our approach achieves near-perfect classification accuracy across four classes—pituitary, glioma, meningioma, and no tumor—us

External IDs:dblp:conf/icaart/ShiraskarVR25