Autism Spectrum Disorder Prediction from Facial Images Using Fine-Tuned Efficient Net B0–B7 Architectures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it