Autism Detection Using Machine Learning

dc.contributor.authorYoucef Kouadria
dc.contributor.authorSakhraoui Zineddine
dc.date.accessioned2025-01-28T09:02:48Z
dc.date.issued2024-09-23
dc.descriptionIn 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
dc.description.abstractThis 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
dc.identifier.issnMM/900
dc.identifier.urihttps://dspace.univ-bba.dz/handle/123456789/5
dc.language.isoen
dc.titleAutism Detection Using Machine Learning
dc.typeThesis

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