Classification of Pneumonia, Tuberculosis and Covid-19 from Chest X-Ray Images Using Convolution Neural Network Model
Bibliographic record
Abstract
Accurate and timely diagnosis of respiratory ailments like pneumonia, tuberculosis (TB), and COVID-19 is pivotal for effective patient care and public health interventions. Deep learning algorithms have emerged as potent tools in medical image classification, offering promise for automated diagnosis and screening. This study presents a deep learning-based approach for categorizing chest X-ray images into three classes: pneumonia, tuberculosis, and COVID-19. Utilizing convolutional neural networks (CNNs) as the primary architecture, owing to their ability to automatically extract relevant features from raw image data. The proposed model is trained on a sizable dataset of chest X-ray images annotated with ground truth labels for pneumonia, TB, and COVID-19. Extensive experiments are conducted to evaluate the model's performance in terms of classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, we compare the performance of our deep learning model with traditional machine learning techniques, including support vector machines, decision trees, XGBoost, and evaluate its performance on an independent test set. Our findings demonstrate that the proposed deep learning model achieves high accuracy in classifying chest X-ray images of pneumonia, TB, and COVID-19, outperforming traditional methods and showing potential for clinical deployment as a screening tool, especially in resource-limited settings.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 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".