Convolutional Neural Networks-Based Approach to Detect Neonatal Respiratory System Anomalies with Limited Thermal Image
Bibliographic record
Abstract
Respiratory system diseases in neonates are thought-about major causes of neonatal morbidity and mortality, particularly in developing countries Early diagnosis and management of these diseases is very important. Thermal imaging stands out as a harmless non-ionizing method, and monitoring of temperature changes or thermal symmetry is used as a diagnostic tool in medicine. This study aims to detect respiratory abnormalities of neonates by artificial intelligence using limited thermal image. Convolutional neural network (CNN) models, although a powerful classification tool, require a balanced and large amount of data. The conditions that require the attention of infants in neonatal intensive care units make medical imaging difficult. It may not always be possible to have much data in the neonatal thermal image database, as in some real-world problems. To overcome this, an effective deep learning model and various data enhancement techniques were used and their effects on the classification results were observed. Neonates with respiratory abnormalities were evaluated in one class, with cardiovascular diseases and abdominal abnormalities were evaluated in the other class. As a result, when the number of images is increased by 4 times with data augmentation, it was determined that the classification accuracy increased from 84.5% to 90.9%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".