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Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201

  • Conference paper
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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

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Abstract

A person with autism or autism spectrum disorder (ASD) has trouble recognizing, socializing, and communicating with others. ASD is not only about one situation. In fact, it has numerous groups of situations. Although it cannot be fully recovered, the right treatments and services can help a person’s symptoms and daily activities. Deep learning has achieved outstanding results in pattern recognition tasks in the current times. CNN-based methods are widely suggested for the research. By taking proper care, children can improve easily. For this, we decided to work on autism in children through image classification for early detection. Early detection can help children provide strength and capability for a better life. It can lessen children’s symptoms and can improve their normal development by assisting them to analyze new capabilities as a way to allow them to be independent for the rest of their lives. To deal with these difficulties, the right guidelines and strategies can help a lot. To provide the necessary guidelines in time, early detection is very important. We employed deep learning techniques to conduct our research utilizing the image dataset. We tried to implement a method that can detect autism in children through facial expressions. In this research, we use VGG 19, Inception V3, and DenseNet 201.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

    Md. Fazlay Rabbi, Fatema Tuz Zohra, Farhana Hossain, Naznin Nahar Akhi, Shakil Khan & Kawsher Mahbub

  2. Department of Computer Science, The University of Alabama at Birmingham, Birmingham, AL, USA

    Milon Biswas

Authors
  1. Md. Fazlay Rabbi
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  2. Fatema Tuz Zohra
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  3. Farhana Hossain
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  4. Naznin Nahar Akhi
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  5. Shakil Khan
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  6. Kawsher Mahbub
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  7. Milon Biswas
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Corresponding author

Correspondence to Md. Fazlay Rabbi .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

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Rabbi, M.F. et al. (2023). Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_14

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  • DOI: https://doi.org/10.1007/978-3-031-23599-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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