Autism Spectrum Disorder Prediction from Facial Images Using Fine-Tuned Efficient Net B0–B7 Architectures
Bibliographic record
Abstract
This research evaluates the effectiveness of the Efficient Net model series (B0–B7) in detecting Autism Spectrum Disorder using facial image data. The findings indicate that the deeper models attain better accuracy and more balanced classification results than the shallower models. EfficientNetB3 and B7 achieve the top accuracy of 0.99, exhibiting excellent precision, recall, and F1-scores for both ASD and non-ASD categories, emphasizing their effectiveness in reducing false positives and false negatives. EfficientNetB2 and B5 also reach competitive accuracies of 0.98 and 0.97, offering dependable options with marginally lower complexity. Conversely, EfficientNetB4 achieves the lowest accuracy of 0.88 because of poor recall in the non-ASD category, indicating restricted generalization. The results affirm that more profound Efficient Net models, especially B3, B5, B6, and B7, excel in feature extraction and classification for ASD prediction, providing a trustworthy structure for the early and precise detection of the disorder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".