Autism Spectrum Disorder Detection Using MobileNet

Authors

  • Surya Teja Arvapalli Jawaharlal Nehru Technological University
  • Sai Abhay A
  • Mounika D
  • Vani Pujitha M

DOI:

https://doi.org/10.3991/ijoe.v18i10.31415

Keywords:

CNN, Transfer learning, Autism, Classification

Abstract


Autism Spectrum Illness (ASD), a evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL),autism can be predicted from a young age.In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected as autism. Here we consider the Autism Master Dataset (AMD) from kaggle.com website, which contains two classes (Autism, Non_Autism). By using this models we are obtaining good accuracy

Covr iJOE Vol. 18 No. 10 (2022)

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Published

2022-07-26

How to Cite

Arvapalli, S. T., A, S. A., D, M., & M, V. P. (2022). Autism Spectrum Disorder Detection Using MobileNet. International Journal of Online and Biomedical Engineering (iJOE), 18(10), pp. 129–142. https://doi.org/10.3991/ijoe.v18i10.31415

Issue

Vol. 18 No. 10 (2022)

Section

Papers

License

Copyright (c) 2022 surya Teja Arvapalli, Mounika D, Sai Abhay A, Vani Pujitha M

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