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Related Concept Videos

Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Related Experiment Video

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Eye Tracking Young Children with Autism
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Improved Transfer-Learning-Based Facial Recognition Framework to Detect Autistic Children at an Early Stage.

Tania Akter1,2, Mohammad Hanif Ali1, Md Imran Khan2

  • 1Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh.

Brain Sciences
|June 2, 2021
PubMed
Summary

An improved MobileNet-V1 model accurately identifies children with autism spectrum disorder (ASD) using facial recognition. This AI tool aids early detection, improving therapeutic strategies for autistic children.

Keywords:
MobileNet-V1autismclassifierclusteringfacial imagestransfer learning

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Area of Science:

  • Computer Science
  • Neuroscience
  • Medical Imaging

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition impacting social and communication skills.
  • Early identification of ASD is crucial for timely and effective intervention strategies.
  • Facial features and eye contact analysis offer potential biomarkers for ASD detection.

Purpose of the Study:

  • To develop an advanced transfer-learning framework for precise early-stage autism face recognition in children.
  • To evaluate the efficacy of various machine learning and deep learning models for ASD identification.

Main Methods:

  • Collected a dataset of children's facial images from Kaggle.
  • Applied and compared multiple machine learning, deep learning, and transfer-learning models.
  • Utilized an improved MobileNet-V1 model for face recognition and k-means clustering for sub-typing.

Main Results:

  • The enhanced MobileNet-V1 model achieved 90.67% accuracy in ASD identification.
  • This model demonstrated superior performance with the lowest fall-out and miss rates compared to other methods.
  • K-means clustering with MobileNet-V1 identified autism sub-types with 92.10% accuracy for k=2.

Conclusions:

  • The proposed transfer-learning framework, particularly the improved MobileNet-V1, shows significant promise for early ASD detection in children.
  • This AI-driven approach can assist clinicians in more explicit and timely diagnosis of autism.
  • Further research could refine the model for broader clinical application and sub-type differentiation.