Autism Spectrum Disorder Detection Using Facial Images and Deep Convolutional Neural Networks
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental disorder, affecting approximately 1% of the global population. It is characterised by deficits in social communication, interaction, and a propensity for repetitive behaviours. Despite its prevalence, the diagnosis of ASD remains challenging due to the lack of conspicuous disparities between neuroimages of affected individuals and their neurotypical counterparts. This study aims to enhance the accuracy and efficiency of ASD diagnosis by integrating deep learning techniques with conventional diagnostic procedures. In this work, we present a novel approach to detect and classify ASD using facial images processed through deep Convolutional Neural Networks (CNNs). We utilised the Visual Geometry Group models (VGG16 and VGG19) to construct our deep learning models. The models were trained and validated using an extensive dataset of facial images. The proposed models have demonstrated promising results, achieving an accuracy rate of 84% in the classification of ASD individuals. This study's findings suggest the potential of deep learning applications in refining the diagnostic process of Autism Spectrum Disorder. Further research is recommended to optimise these models and validate their effectiveness on a broader scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".