Lung-Related Diseases Classification Using Deep Convolutional Neural Network
Bibliographic record
Abstract
Accurate diagnosis is a crucial first step in the management and treatment of lung diseases, which include infectious diseases such as COVID-19, viral pneumonia, lung opacity, tuberculosis, and bacterial pneumonia. Despite these conditions sharing similar manifestations in chest X-ray images, it is imperative to correctly identify the disease present. This study, therefore, sought to develop a convolutional neural network (CNN)based model for the classification of lung diseases. Four distinct CNN models, namely MobileNetV2, ResNet-50, ResNet-101, and AlexNet, were rigorously evaluated for their ability to classify lung diseases from chest X-ray images. These models were tested against three classification schemes to examine the impact of high interclass similarity: a 4-subclass classification (COVID-19, viral pneumonia, lung opacity, and normal), a 5-subclass classification (COVID-19, viral pneumonia, lung opacity, tuberculosis, and normal), and a 6-subclass classification (COVID-19, lung opacity, viral pneumonia, tuberculosis, bacterial pneumonia, and normal). The retrained ResNet-50 architecture yielded the best results, achieving a classification accuracy of 97.22%, 92.14%, and 96.08% for the 6-subclass, 5-subclass, and 4-subclass classifications respectively. Conversely, ResNet-101 demonstrated the lowest classification accuracy for the 6subclass and 5-subclass classifications, with 78.12% and 79.49% respectively, while MobileNetV2 had the lowest accuracy for the 4-subclass classification, with 88.89%. These results suggest that, despite high interclass similarity, the ResNet-50 model can effectively classify lung-related diseases from chest X-ray images. This finding supports the use of computer-aided detection (CAD) systems as decision-support tools in the early classification of lung-related diseases.
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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 | 0.000 | 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) | 0.001 | 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".