Metric-Based Frame Selection and Deep Learning Model With Multi-Head Self Attention for Classification of Ultrasound Lung Video Images
Bibliographic record
Abstract
Detection of COVID-19 manifestations in lung ultrasound (US) images has gained attention in recent times. The current state-of-the-art technique for distinguishing a healthy lung from COVID-19 infected or bacterial pneumonia infected one uses non-adjacent frames or equally spaced frames from the video. However, the frame content or correlation between the selected frames has not been taken into consideration for frame selection. In this paper, a metric-based frame selection approach is proposed for three-way classification of lung US videos, and the influence of the frame selection method on image classification accuracy is studied. A deep learning model comprising of a pre-trained model (VGG16) for feature extraction, multi-head attention for feature calibration, global averaging for feature reduction, and a dense layer for classification is proposed. The pre-trained model is re-trained using cross-entropy loss with balanced weights to handle class imbalance. Two types of classification approaches are considered: i) few frames in a video are selected using the proposed metrics; and (ii) all frames in a video are considered. With VGG16 as the pre-trained model, a mean balanced sensitivity of COVID-19, bacterial pneumonia, and healthy classes with 0.82, 0.89, and 0.87, respectively was achieved using 5-fold cross-validation. The results showthat even random selection of frames performs better than fixed frame selection and the proposed frame selection method outperforms the state-of-art fixed frame selection irrespective of the type of backbone model used for lung US classification.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".