Lung Disease Detection and Classification with Deep Learning Approach
Bibliographic record
Abstract
Nowadays, COVID-19 outbreak and respiratory symptoms globally take a huge number of people's lives away. Especially, COVID-19, which is a pandemic initially spreading out in the first quarter of the year 2020, heavily affects many people to die. Most countries have tried to find ways to solve and mitigate this outbreak including respiratory diseases due to the mentioned reason. We also face with insufficient number of medical personnel and equipment to treat the diseases. The need of technology to analyze the images for the disease detection is quite a challenge. In this work, we consider detecting and classifying many lung diseases from chest X-ray images using a deep learning (artificial intelligence) approach with VGG16 models. The lung diseases are COVID-19, Pneumonia and Pneumothorax. We use quite large published disease datasets. Our detection and classification models give impressive results providing between 93% and 100% accuracy, precision, recall and F1-measure.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".