Enhancing Autism Diagnosis Through Machine Learning
Abstract & Details
Research Area
Computer Science Engineering
Keywords
Autism spectrum disorder
Machine learning
VGG16
InceptionV3
Stream lit application
Diagnosis.
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by challenges with social interaction, communication, and repetitive behaviours. Early diagnosis and intervention are crucial for improving outcomes for individuals with ASD. This research investigates the potential of machine learning techniques, specifically the VGG16 and InceptionV3 models, to enhance the accuracy and efficiency of autism diagnosis. By leveraging these models, the research aims to develop a novel approach for automated ASD detection based on facial features extracted from images uploaded via a Stream lit application interface. The trained models will analyze the uploaded facial images and provide a quantitative assessment of the likelihood of ASD diagnosis. The results will be displayed in the Stream lit application interface, providing clinicians and caregivers with valuable insights for early intervention and treatment planning.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Prof.Mahalakshmi C V | Bangalore Institute of Technology |
| 2 | Sneha C J | Bangalore Institute of Technology |
| 3 | Sowmya Kanchan | Bangalore Institute of Technology |
| 4 | Tejashwini A M | Bangalore Institute of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
V, Prof.Mahalakshmi C, J, Sneha C, Kanchan, Sowmya, & M, Tejashwini A (2024). Enhancing Autism Diagnosis Through Machine Learning. International Journal of Advance Research and Innovative Ideas In Education, 10(3), 533-536.
MLA Style
V, Prof.Mahalakshmi C, et al. "Enhancing Autism Diagnosis Through Machine Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, 2024, pp. 533-536.
IEEE Style
Prof.Mahalakshmi C V, Sneha C J, Sowmya Kanchan, and Tejashwini A M, "Enhancing Autism Diagnosis Through Machine Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, pp. 533-536, 2024.
Vancouver Style
V Prof.Mahalakshmi C, J Sneha C, Kanchan Sowmya, M Tejashwini A. Enhancing Autism Diagnosis Through Machine Learning. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(3):533-536.
Harvard Style
V, Prof.Mahalakshmi C, J, Sneha C, Kanchan, Sowmya, & M, Tejashwini A (2024) 'Enhancing Autism Diagnosis Through Machine Learning', International Journal of Advance Research and Innovative Ideas In Education, 10(3), pp. 533-536.
Chicago Style
V, Prof.Mahalakshmi C, et al. "Enhancing Autism Diagnosis Through Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 533-536.
Turabian Style
V, Prof.Mahalakshmi C, et al. "Enhancing Autism Diagnosis Through Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 533-536.
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