Autism Detection Using Machine Learning
Date
2024-09-23
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Abstract
This research investigates the use of machine learning, specifically deep learning, for the
detection of Autism Spectrum Disorder (ASD) using facial image data. By employing the
VGG16 model with transfer learning, the project achieved high classification accuracy,
demonstrating the potential of machine learning in supporting early ASD diagnosis. The use
of automated image analysis provides a non-invasive, scalable solution that could
complement traditional diagnostic methods. The results indicate that machine learning can
significantly contribute to healthcare by enabling quicker and more accurate ASD diagnoses.
Future improvements could include the use of larger datasets and multimodal data such as
voice and behavioral analysis
Description
In this work, we explored the use of machine learning, particularly deep learning, for the
detection of Autism Spectrum Disorder (ASD) using facial image data. By leveraging the
power of transfer learning with the pre-trained VGG16 model, we were able to achieve a
high degree of accuracy in classifying facial images of individuals as either autistic or nonautistic. The model demonstrated the potential of deep learning techniques in improving
diagnostic processes for ASD, offering a non-invasive, efficient, and scalable solution for
early diagnosis. While our results are promising, further research is needed to improve
generalization by using larger datasets and integrating multimodal data, such as voice
and behavioral patterns. Future advancements in this field can significantly contribute to
the healthcare industry by enabling quicker and more accurate diagnoses for individuals
on the autism spectrum