A Deep Learning Model for Brain Tumour Classification: An Advanced Analysis of Multi-Modal MRI Integration

This research discusses recent breakthroughs in brain tumour categorisation using MRI and a deep learning algorithm. Traditional brain tumour classifications cannot characterise complicated tumours similarly or use multimodal MRI data extensively. To overcome these constraints, Structural MRI, Functional MRI, Diffusion-Weighted Imaging (DWI), and Magnetic Resonance Spectroscopy (MRS) are rigorously explored to improve tumour comprehension. This paper implements CNNs, RNNs, and hybrid architectures for deep learning. These models are skilled at managing multimodal MRI data with subtlety, providing a more complete view of brain tumours. The review reveals that standard classification approaches fail. It emphasises the need for powerful computational techniques for complex and unpredictable brain tumour data.

I. Introduction

The full form of BRAINS is Behavior Replication by Analog Instruction of the Nervous System. A brain is an important organ of human body which controls the nervous system and functions of body. The brain has three major parts: the cerebrum, the cerebellum, and the brainstem and is presented in the Figure 1 is referenced from the cite [https://ncimedia.cancer.gov/pdq/media/images/680 399571.jpg]. Figure 1:

Brain imaging