Deep Learning-Based Segmentation and Classification of COVID-19 Infection Severity Levels from CT Scans
Bibliographic record
Abstract
Due to the emergence of coronavirus disease 2019 (COVID-19) around the globe, the detection and treatment of COVID-19 patients is an extremely essential process in healthcare systems. Among many imaging technologies, Computed Tomography (CT) analyses with deep learning frameworks offer better efficiency than the other imaging modalities. To segment the infected COVID-19 Region-Of-Interests (ROIs), an Enriched 2.5D U-Net (E2.5D U-Net)-based deep learner has been used. In contrast, it requires categorization of infection severity to identify the patients in the prior stage. Hence, in this article, feature extraction with a classification framework is proposed to learn deep features related to the infection disease severity. In this framework, each segment from the E2.5D U-Net is fed to a deep learner such as DenseNet201 to extract the deep features. These features are learned independently by different machine learning classifiers to categorize the infection severity levels. It aids physicians in diagnosing COVID-19 patients in advance. But it needs other infection-related features to enhance the efficiency. Therefore, a multi-modeling classification framework is proposed. This framework extracts the handcrafted features from CT scans and concatenates them with the deep features to get the unified feature vector. Moreover, these feature vectors are trained by using the multi-modeling classifier for predicting the infection severity levels with higher accuracy. At last, the testing outcomes exhibit that the multi-modal classifier establishes a higher efficiency than the standard classifier frameworks.
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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 | 0.002 |
| 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 | 0.001 |
| 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".