Detection of COVID-19 Cases from X-ray Images Using Capsule-based Network
Bibliographic record
Abstract
Coronavirus (Covid-19) disease has spread abruptly all over the world since the end of 2019. Computed Tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions has been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate identification of covid-19 cases. however, CNNs lose important information in intermediate layers and require large datasets. In this paper, capsule network (CAPSNET) is used. Capsule network performs better than CNNs for small datasets. an accuracy of 0.9885, F1-score of 0.9883, precision of 0.9859, recall of 0.9908 and area under the curve (AUC) of 0.9948 are achieved on the capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease the overfitting. Accordingly, a dropout rate of 0.1 shows best results. finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".