1) The document presents research on developing a machine learning approach to diagnose Autism Spectrum Disorder (ASD) based on analyzing behaviors in videos of infants ages 6 to 36 months. 2) Researchers used over 1,700 videos of 365 infants that were annotated and split into training, validation, and test sets. Deep learning techniques were applied to images extracted from videos and to facial keypoint features to diagnose ASD. 3) Results showed the approaches addressed data imbalances and achieved ASD diagnosis, with future work planned on incorporating temporal behavior patterns, audio, and self-supervised learning methods.

2. Presented by:
Tabinda Islam
700-FBAS/BSBI/F20
Department of BioInformatics
Faculty of Computing
International Islamic University Islamabad (IIUI)
Presented to:
Mr. Farhan Khan
Total Pages: 8
Digital Image Processing
2

3. Table of Contents Title Introduction Methodology Results Limitations
Title
Results
Methodology
Introduction
Limitations
3

4. Machine Learning Based Autism Spectrum Disorder
Detection from Videos
Chongruo, et al. (2020) published on IEEE
Research of University of california
Supported by National Institute of Mental Health of the National Institutes of Health
In colaboration withUC Davis 2019
College of Engineering Dean’s Collaborative Research (DECOR) Award.
Table of Contents Title Introduction Methodology Results Limitations

5. • Autism Spectrum Disorder (ASD) is one of the most prevalent developmental disorders[1]
• Affecting 1 out of 54 children according to a recent estimate[1]
• For every 1 Girl , 3.8 Boys wese identified[1]
• Deficiency in social communication and show atypical attention to others and objects[3]
• Early diagnosis of ASD is crucial for best outcomes to interventions
• Aim: Machine learning (ML) approach to ASD diagnosis based on identifying specific
behaviors from videos of infants of ages 6 through 36 months
Table of Contents Title Introduction Methodology Results Limitations
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6. Table of Contents Title Introduction Methodology Results Limitations
6
Data
Collection
1,707 videos of
365 infants 3min
durations (6-36
month)
Data spliting
Test set Train set validation set
applying different approches on
these data
Data analysis
(Observer 5.0 by Noduls)
Does coder reach 90%
aggrement?
Double coding for 15% data for
realibility
Interclass correlation value is
satisfactory?
ye
s
yes
Training
After
annotation 547
videos of 133
subjects were
selected on
frame level it
is 2.95M
yes
No
Based on Image
trained by SGD optimizer
images Resized to 224x224 and
horizontally flipping during traing
ResNet-18 and Image Net
Balancing the data
Based on facial
keypoints as features
Extracted by OpenFace library
2 modals used : 1D and 2D
Trained by using Adam Optimizer
ASD diagnosis ML
Deep learning techniques
Parametric
Non-Parametric
Balancing the data by
SMOTE and Tomek
links
Result
evaluation

7. Table of Contents Title Introduction Methodology Results Limitations
7
The statistics of Test , Train and
Validation datasets
Results of Image Based Results of Feature Based
Results of ASD Diagnosis ML

8. Table of Contents Title Introduction Methodology Results Limitations
8
• Addressed class imbalance
• Future Plans: Temporal dimension for behavior, audio for vocalization,
self-supervised schemes for detection

9. • Data Statistics on Autism Spectrum Disorder. [Online]. Available:
https://www.cdc.gov/ncbddd/autism/data.html
• L. Ronconi, M. Devita, M. Molteni, S. Gori, and A. Facoetti, “Brief report: When
large becomes slow: Zooming-out visual attention is associated to orienting
deficits in autism,” Journal of autism and developmental disorders, vol. 48, no. 7,
pp. 2577–2584, 2018.
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References

10. 10