Lung-Related Diseases Classification Using Deep Convolutional Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Accurate diagnosis is a crucial first step in the management and treatment of lung \ndiseases, which include infectious diseases such as COVID-19, viral pneumonia, lung \nopacity, tuberculosis, and bacterial pneumonia. Despite these conditions sharing similar \nmanifestations in chest X-ray images, it is imperative to correctly identify the disease \npresent. This study, therefore, sought to develop a convolutional neural network (CNN)- \nbased model for the classification of lung diseases. Four distinct CNN models, namely \nMobileNetV2, ResNet-50, ResNet-101, and AlexNet, were rigorously evaluated for \ntheir ability to classify lung diseases from chest X-ray images. These models were tested \nagainst three classification schemes to examine the impact of high interclass similarity: \na 4-subclass classification (COVID-19, viral pneumonia, lung opacity, and normal), a \n5-subclass classification (COVID-19, viral pneumonia, lung opacity, tuberculosis, and \nnormal), and a 6-subclass classification (COVID-19, lung opacity, viral pneumonia, \ntuberculosis, bacterial pneumonia, and normal). The retrained ResNet-50 architecture \nyielded the best results, achieving a classification accuracy of 97.22%, 92.14%, and \n96.08% for the 6-subclass, 5-subclass, and 4-subclass classifications respectively. \nConversely, ResNet-101 demonstrated the lowest classification accuracy for the 6- \nsubclass and 5-subclass classifications, with 78.12% and 79.49% respectively, while \nMobileNetV2 had the lowest accuracy for the 4-subclass classification, with 88.89%. \nThese results suggest that, despite high interclass similarity, the ResNet-50 model can \neffectively classify lung-related diseases from chest X-ray images. This finding \nsupports the use of computer-aided detection (CAD) systems as decision-support tools \nin the early classification of lung-related diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it