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ASD-GResTM: Deep Learning Framework for ASD classification using Gramian Angular Field.

Fahad Almuqhim1, Fahad Saeed1

  • 1Knight Foundation School of Computing and Information Sciences (KFSCIS), Florida International University (FIU), Miami, FL, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|July 18, 2024
PubMed
Summary
This summary is machine-generated.

A new deep-learning model, ASD-GResTM, uses functional magnetic resonance imaging (fMRI) data to accurately classify Autism Spectrum Disorder (ASD) in children. This novel approach transforms fMRI time-series data into images, improving diagnostic reliability over traditional methods.

Keywords:
ASDGAFLSTMResNetdeep learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Developmental Psychology

Background:

  • Autism Spectrum Disorder (ASD) diagnosis relies on behavioral metrics with high variability.
  • Neuroimaging and machine learning offer potential for more reliable ASD assessment.
  • Current diagnostic tools are limited by environmental and comorbid factors.

Purpose of the Study:

  • To develop a deep-learning model for classifying Autism Spectrum Disorder (ASD) using functional magnetic resonance imaging (fMRI) data.
  • To introduce a novel method for transforming fMRI time-series data into Gramian Angular Fields (GAF) for image-based analysis.
  • To create a reliable and quantifiable diagnostic tool for ASD.

Main Methods:

  • Developed ASD-GResTM, a deep-learning framework utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) layers.
  • Transformed fMRI time-series data into Gramian Angular Field (GAF) images.
  • Employed a single-layer perceptron (SPL) for final ASD classification.
  • Utilized the openly available ABIDE-I dataset for training, validation, and testing.

Main Results:

  • The ASD-GResTM model achieved high accuracy across four centers.
  • Outperformed state-of-the-art models on two centers, with accuracy increases of 17.58% and 6.7%.
  • Maximum accuracy reached 81.78% with high sensitivity and specificity.

Conclusions:

  • The proposed ASD-GResTM framework demonstrates a promising, accurate, and reliable method for ASD classification using fMRI data.
  • Transforming fMRI data into GAF images enables effective application of deep learning in neuroimaging analysis.
  • This approach offers a more quantifiable alternative to traditional behavioral assessments for ASD.