Efficient Deep Learning Approach for the Classification of Pneumonia in Infants from Chest X-Ray Images
Bibliographic record
Abstract
Pediatric pneumonia is a major infectious disease which caused more than 5,00,000 deaths of infants and young children below the age of 5 years.This number is equivalent to the death of one child per minute.These statistics are daunting and more focused studies and development of tools are required to tackle this challenge.It has also been seen that pediatric pneumonia is curable through antibiotics and oxygen therapy if it is diagnosed at early stages.Current radiology techniques used are not able to diagnose pneumonia at early stages because specific arrangements are to be made for childcare.They cannot be treated as normal adults.Special care is to be taken for childcare as improper handling of radiology techniques may harm the child or generate inaccurate diagnosis.Recent advancements in computer aided diagnosis with the help of deep learning techniques has improved the quality of medical imaging techniques such as CT-Scan, X-ray images, etc.However, limited attention is given to the application of deep learning techniques for diagnosis and classification of pediatric pneumonia.Moreover, conducting manual tests, image classification and analysis of radiological images are prone to human errors due to lack of expertise of the radiologists.Inaccurate analysis of radiological images like X-rays can suggest inappropriate treatment for children which can prove to be fatal.To fasten the interpretation of radiological images, this paper suggests a deep learning model, focusing specifically on classifying pediatric pneumonia from chest X-ray images.In this paper, a MobileNeT-V3 architecture is implemented and tested against various datasets containing more than 10,000 chest X-ray images.Analysis of suggested implementation shows that this is better than various classical tools and techniques used for classification of chest X-ray.The suggested technique achieved a classification accuracy of 95.8% over dataset-1 and 97.8% over dataset-2, shows the efficiency of the technique.The model in this study not only demonstrated high classification accuracy but also excelled in other key metrics, achieving a precision of 97% and 94%, recall of 97% and 98%, and F1 scores of 97% for both datasets, underscoring its precision and reliability in diagnosing pediatric pneumonia.
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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 | 0.000 |
| 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 | 0.000 | 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".