A deep convolutional neural network model for medical data classification from computed tomography images
Bibliographic record
Abstract
Abstract Machine learning provides powerful techniques for several applications, including automated disease diagnosis through medical image classification. Recently, many studies reported that deep learning approaches have demonstrated significant performance and accuracy improvements over shallow learning techniques. The deep learning approaches have been used in many problems related to disease diagnoses, such as thyroid diagnosis, diabetic retinopathy detection, foetal localization, and breast cancer detection. Many deep learning methods have been reported in the recent past that uses medical images from various sources, such as healthcare providers and open data initiatives, and reported significant improvement in terms of precision, recall, and accuracy. This paper proposes a framework incorporating deep convolutional neural networks and an enhanced feature extraction technique for classifying medical data. To show the real‐world usability of the proposed approach, it has been used for the classification of COVID‐19 images from computed tomography scans. The experimental results show that the proposed approach outperformed some of the chosen baselines and obtained an accuracy of 98.91%, comparable with already reported accuracies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".