Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method
Bibliographic record
Abstract
The new coronavirus, which emerged in early 2020, caused a major global health crisis in 7 continents.An essential step towards fighting this virus is computed tomography (CT) scans.CT scans are an effective radiological method to detecting the diagnosis in early stage, but have greatly increased the workload of radiologists.For this reason, there are systems needed that will reduce the duration of CT examinations and assist radiologists.In this study, a two-stage system has been proposed for COVID-19 detection.First, a hybrid method is proposed that can segment the infected region from CT images.The reason for this is that there is not always a reference image in the datasets used in the classification.For this purpose; UNet, UNet++, SegNet and PsPNet were used both separately and as hybrids with GAN, to automatically segment infected areas from chest CT slices.According to the segmentation results, cGAN-UNet hybrid system was selected as the most successful method.Experimental results show that the proposed method achieves a segmentation success with a dice score of 92.32% and IoU score of 86.41%.In the second stage, three classifiers which include a Convolutional Neural Network (CNN), a PatchCNN and a Capsule Neural Network (CapsNet) were used to classify the generated masks as either COVID-19 or not, using the segmented images obtained from cGAN-UNet.Success of these classifiers was 99.20%, 92.55% and 73.84%, respectively.According to these results, the highest success was achieved in the system where cGAN-Unet and CNN are used together.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".