Summary of Towards Equitable Asd Diagnostics: a Comparative Study Of Machine and Deep Learning Models Using Behavioral and Facial Data, by Mohammed Aledhari et al.
Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data
by Mohammed Aledhari, Mohamed Rahouti, Ali Alfatemi
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates machine learning models to improve Autism Spectrum Disorder (ASD) diagnosis in females, who often go undiagnosed due to gender-specific symptoms not considered by conventional diagnostics. The authors evaluate Random Forest and convolutional neural networks for enhancing ASD diagnosis through structured data and facial image analysis. Notably, Random Forest achieves 100% validation accuracy across datasets, highlighting its ability to manage complex relationships and reduce false negatives. In contrast, MobileNet outperforms the baseline CNN in image-based analysis, achieving 87% accuracy, though a 30% validation loss suggests possible overfitting. To address these limitations, future work will focus on hyperparameter tuning, regularization, and transfer learning. The study’s findings suggest that Random Forest’s high accuracy and balanced precision-recall metrics could enhance clinical workflows. Additionally, MobileNet’s lightweight structure shows promise for resource-limited environments, enabling accessible ASD screening. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help diagnose Autism Spectrum Disorder (ASD) in girls more accurately. Right now, girls are often missed because their symptoms are different from those of boys with ASD. The study uses machine learning models to analyze data and pictures to improve diagnosis. One model, Random Forest, does a great job of identifying people with ASD correctly and reducing mistakes. Another model, MobileNet, also does well, but needs some tweaks to work better in real-life situations. The researchers think that combining behavioral data with facial analysis could make the diagnosis process even more accurate. This study shows that machine learning models like Random Forest can help doctors diagnose ASD more accurately and efficiently. |
Keywords
» Artificial intelligence » Cnn » Hyperparameter » Machine learning » Overfitting » Precision » Random forest » Recall » Regularization » Transfer learning
