COVID-19 Self-Test Guidance System For Swab Collection Using Deep Learning
Bibliographic record
Abstract
The COVID-19 rapid antigen self-test kits are widely administered in several countries to increase the testing frequency and reduce the load on clinics for in-person tests. Yet, the telehealth worker supervision is mandatory to ensure proper sampling procedure is followed and high-quality swab samples are taken. To reduce the load on the health workers in telehealth, we propose a system that eliminates the need for any human supervision by guiding the testers throughout the self-test to ensure the collection of high-quality swab samples. The proposed system takes a live video stream of the frontal face of a user as input and provides real-time instructions to do the self-test correctly with corrective actions when detecting wrong steps. This is mainly done using a collection of deep learning (DL) models. The system uses a novel swab position classification model, Small-MobileNetV2 with Depth-Wise Attention (S-MBNV2-DWAtt), to detect whether a swab is in one of the nostrils or not, which is an optimized version of MobileNetV2 in terms of parameter count and inference speed. The depth-wise attention block allows it to focus on specific parts of the images where the swabs would possibly lie. Lastly, a large-scale synthetic dataset is created to increase the generalization to a variety of swabs and users and a small real dataset is collected to finetune the model on scenes that are similar to the deployment scenarios. The proposed swab position classification model is found to have outstanding performance in terms of both accuracy and speed; it outperforms the ResNet and VGG architectures by 22.83% and 35.11% respectively on a real-world test set while operating at 25 FPS on CPU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".