Pneumonia Detection Using Deep Learning: A CNN-Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pneumonia is a deadly lung infection which can lead to life-threatening complications if left undiagnosed and untreated.Traditional diagnosis depends on radiologists reading chest X-rays manually, a time-consuming process prone to human error.Mistakes in diagnosis causes delayed or improper treatment and severe health impacts or even fatality.Following the growth of deep learning methods, automatic medical image analysis is becoming an increasingly potential means to enhance the accuracy and efficiency of diagnoses.To tackle these challenges, we propose a deep learning-based model for automated pneumonia detection using Convolutional Neural Networks (CNNs).Our research leverages the publicly available chest X-ray dataset from Kaggle to train a custom CNN model that includes three convolutional layers, batch normalization, dropout regularization, and an Adam optimizer.The model achieved an impressive test accuracy of 85.74%, showcasing its potential to aid in clinical decision-making.Additionally, this study looks into how data augmentation affects performance and considers ways to improve the model's generalization and robustness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it