Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs

TL;DR

This paper introduces a novel multi-modal 3D CNN approach combined with GAN-based data augmentation to improve the accuracy of ADHD classification from fMRI data, outperforming single-modal models.

Contribution

It presents a new multi-modal 3D CNN architecture with GAN-based data augmentation for enhanced ADHD diagnosis from fMRI data.

Findings

Multi-modal 3D CNN improves classification accuracy.

GAN-based data augmentation enhances model performance.

Comparison shows multi-modal approach outperforms single-modal models.

Abstract

This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new techniques it is possible to preserve the 3D spatial data of fMRI data. Additionally there have been recent advances in the use of 3D generative adversarial neural networks (GANs) for the generation of normal MRI data. This work utilizes multi modal 3D CNNs with data augmentation from 3D GAN for ADHD prediction from fMRI. By leveraging a 3D-GAN it would be possible to use deepfake data to enhance the accuracy of 3D CNN classification of brain disorders. A comparison will be made between a time distributed single modal 3D CNN model for classification and the modified multi modal model with MRI data as well.