Pleural Effusion Measurement Method on Thoracic Image of Dengue Fever Patient Using Image Processing Technique
Bibliographic record
Abstract
Pleural effusion is the accumulation of fluid in the pleural cavity.The pleural cavity is located between the pleural layer that covers the lungs and the pleural layer that attaches to the inner wall of the chest cavity.This condition generally occurs due to complications of the disease, including Dengue Hemorrhagic Fever (DHF).Information about the severity of DHF suffered by patients is very important to determine therapy and patient observation because improper handling can lead to patient death.Pleural Efusion Index (PEI) is an indicator used to determine the severity of DHF.So far, doctors or radiologists have measured PEI using conventional chest X-rays manually with a ruler.This method is complicated and time-consuming and must be performed by a specialist such as a doctor or radiologist.Meanwhile, doctors or radiologists who can measure PEI are not available all the time.Therefore, an automatic, fast, and accurate system is needed that can measure PEI and is practical for everyone so that DHF treatment can be carried out quickly.In this study, we developed a PEI measurement method automatically using image processing techniques using digital chest images.We implemented this method on a small Raspberry Pi computer so it can become a portable system.With this portable system, a doctor or radiologist can find out the PEI value and at the same time see the patient's condition directly at the treatment site.This system can also measure PEI directly from conventional film images using an attached camera.The design of the computer program includes preprocessing stages, determining the width of the hemithorax, segmentation, and finally measuring the PEI.The material used in this study was 20 chest images of DHF patients suffering from pleural effusion.The correlation between measurements using this system and manual measurements produces a value of 0.70 which means a high correlation.
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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".