Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network
Bibliographic record
Abstract
Background: Convolutional Neural Networks (CNN) have provided a significant achievement in different machine learning tasks such as speech recognition, image classification, automotive software engineering, together with some substantial applications in neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method In this paper, we focused on the diagnosis of the autism spectrum disorder (ASD) via CNN using a large brain imaging dataset. We classified ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data represented by a multi-site database known as Autism Brain Imaging Data Exchange (ABIDE). The proposed approach was able to classify individuals with autism compared to typical controls based on the patterns of functional connectivity. The outcome measure is accuracy, sensitivity, and specificity of the prediction of ASD from control subjects. Results: The experimental results indicate that our proposed model with 70.22 % diagnostic accuracy in classification of the ASD outperforms the previous works on ABIDE I dataset and for the CC400 functional parcellation atlas of the brain. Also, it was shown that the number of parameters used in our CNN model is fewer than the best known study in the ASD classification which leads to the reduction of the training time. The existing best-known method had a huge number of parameters, 19,961,200, in theirs final stage wheras we reduced it to 4,398,80221 parameters. The sensitivity and specificity were also measured in this study as part of our report
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".