Deep Learning-Based Methodology for Early Detection of Autism Spectrum Disorder

Deep Learning-Based Methodology for Early Detection of Autism Spectrum Disorder

Registration ID: IJNRD_222578

Published ID: IJNRD2407078

DOI: Click Here to Get

Aakriti Bahl , Dr.Priyanka Gupta

Autism Spectrum Disorder (ASD), Deep Learning (DL), Neural Networks, neurodevelopmental disorders, Behavioural analysis, Neuroimaging, Genetic data, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVMs), Random Forests.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disease characterised by difficulties in social interaction, communication, behaviour, and sensory processing. Early identification is important because it allows for prompt treatments, which can alter an individual's lifelong trajectory and general well-being. However, the complications in detecting ASD in its early stages persist due to the dependence on subjective judgements and clinical observations. These old techniques may miss tiny indications throughout important developmental phases, perhaps resulting in delayed intervention. As a result, there is a vital need to research creative approaches to improve the accuracy and capability of early ASD identification, for example, deep machine learning. By leveraging the power of data-driven analysis, such as behavioural patterns, neuroimaging findings, and genetic insights, deep learning could provide a revolutionary avenue for more precise and timely interventions, ultimately improving the outcomes and quality of life for individuals with ASD. This chapter will propose a deep learning-based approach for the early diagnosis of Autism, with the potential to automate and enhance the disorder's early diagnosis while also allowing for faster therapy, resulting in better outcomes for persons with ASD. The combination of deep learning technology with the challenges of early ASD identification provides a promising approach to improving the lives of people on the autistic spectrum. By exploiting the power of deep learning, we are on the verge of revolutionising the identification, diagnosis, and support of persons with ASD, which will eventually lead to better results and a more inclusive society.

Published Paper   E-Certificate

"Deep Learning-Based Methodology for Early Detection of Autism Spectrum Disorder", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 7, page no.a756-a763, July-2024, Available :https://ijnrd.org/papers/IJNRD2407078.pdf

Volume 9 Issue 7, July-2024

Pages : a756-a763

Paper Reg. ID: IJNRD_222578

Published Paper Id: IJNRD2407078

Downloads: 000121181

Research Area: Computer Engineering 

Country: rajpura, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407078.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407078

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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