Open Access

ARTICLE

Conditional Generative Adversarial Network Approach for Autism Prediction

by K. Chola Raja^1,*, S. Kannimuthu²

1 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, 641202, Tamilnadu, India
2 Department of Information Technology, Karpagam College of Engineering, Coimbatore, 641032, Tamilnadu, India

* Corresponding Author: K. Chola Raja. Email:

Computer Systems Science and Engineering 2023, 44(1), 741-755. https://doi.org/10.32604/csse.2023.025331

Received 20 November 2021; Accepted 06 January 2022; Issue published 01 June 2022

Abstract

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.

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