Tuberculosis and Pneumonia Detection Using CNN
Bibliographic record
Abstract
Artificial intelligence, particularly machine learning, is revolutionizing numerous fields by either supplementing or replacing human efforts, leading to enhanced efficiency and autonomy in systems. Healthcare stands as a notable domain ripe for collaboration with AI and machine learning, offering smoother and more efficient operations. In the context of the modern era, characterized by a scarcity of quality radiologists, the demand for AI-driven solutions in chest X-ray-based disease detection has become increasingly imperative. The subject of this paper is the classification of two major chest diseases, Pneumonia and Tuberculosis, through the implementation of advanced neural network architectures, specifically VGG19 and Convolutional Neural Network (CNN). The system provides diagnostic opinions to users, aiding medical professionals in making prompt and informed decisions about the presence of diseases. In comparison to prior research, this proposed model showcases the capability to detect two types of abnormalities, accurately discerning whether an X-ray is normal or exhibits abnormalities associated with pneumonia and tuberculosis. The VGG19-based CNN achieves remarkable accuracy of 96.87% for Pneumonia and 99.52% for Tuberculosis, surpassing previous models. This advancement underscores the potential of leveraging state-of-the-art neural network architectures for precise and efficient disease classification, addressing the critical need for accurate diagnostic tools in the medical field.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".