Can I read Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data on EtoBox?

Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data by Vidya, Suryansh; Gupta, Kush; Aly, Amir; Wills, Andy; Ifeachor, Emmanuel; Shankar, Rohit is a scholarly article available to read on EtoBox.

What is Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions Using fMRI Data about?

Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been shown to significantly improve the quality of life of autistic individuals. However, diagnostics methods for ASD rely on assessments based on clinical presentation that are prone to bias and can be challenging to arrive at an early diagnosis. There is a need for objective biomarkers of ASD which can help improve diagnostic accuracy. Deep learning (DL) has achieved outstanding performance in diagnosing diseases and conditions from medical imaging data. Extensive research has been conducted on creating models that classify ASD using resting-state functional Magnetic Resonance Imaging (fMRI) data. However, existing models lack interpretability. This research aims to improve the accuracy and interpretability of ASD diagnosis by creating a DL model that can not only accurately classify ASD but also provide explainable insights into its working. The dataset used is a preprocessed version of the Autism Brain Imaging Data Exchange (ABIDE) with 884 samples. Our findings show a model that can accurately classify ASD and highlight critical brain regions differing between ASD and typical controls, with potential impli

Author

Vidya, Suryansh; Gupta, Kush; Aly, Amir; Wills, Andy; Ifeachor, Emmanuel; Shankar, Rohit

Published

2024

Language

EN