Intelligent Systems in Neuroimaging: Pioneering AI Techniques for Brain Tumor Detection

TL;DR

This paper explores advanced AI techniques, particularly deep learning models, for brain tumor classification in MRI images, achieving high accuracy and efficiency to support clinical diagnosis.

Contribution

It introduces a hybrid approach combining custom convolutional and pre-trained neural networks, demonstrating superior performance in tumor classification tasks.

Findings

Xception architecture achieved 98.71% accuracy

Models showed high generalization to unseen data

Reduced computational complexity for clinical use

Abstract

This study deliberates on the application of advanced AI techniques for brain tumor classification through MRI, wherein the training includes the present best deep learning models to enhance diagnosis accuracy and the potential of usability in clinical practice. By combining custom convolutional models with pre-trained neural network architectures, our approach exposes the utmost performance in the classification of four classes: glioma, meningioma, pituitary tumors, and no-tumor cases. Assessing the models on a large dataset of over 7,000 MRI images focused on detection accuracy, computational efficiency, and generalization to unseen data. The results indicate that the Xception architecture surpasses all other were tested, obtaining a testing accuracy of 98.71% with the least validation loss. While presenting this case with findings that demonstrate AI as a probable scorer in brain tumor diagnosis, we demonstrate further motivation by reducing computational complexity toward real-world clinical deployment. These aspirations offer an abundant future for progress in automated neuroimaging diagnostics.