FPGA based Fuzzy Edge Detection System for COVID-19 X-Ray Images
Bibliographic record
Abstract
COVID-19 pandemic has caused a global crisis, with widespread loss of life and severe health issues, including lungs damage.COVID-19 diagnosis typically obtains by either chest X-ray images, which require a precise diagnosis, or a polymerase chain reaction test, which needs long-time.Rapid and precise diagnosis is crucial for preserving lives.This study aims to present an efficiently and accurately system that is capable to identify edges in X-ray images, allowing prompt and precise diagnosis.The fuzzy image processing methods is utilized in MATLAB to identify edges of the images.This stage is performed for verification purposes and to compare the findings with hardware platform outcomes.The hardware platform is implemented by High-Level Synthesis technology as a softwareplatform and the ZYNQ-7000 kit as a hardware-platform.Software and hardware platforms combination produce precise edges and faster processing time compared with the results obtained by MATLAB.Higher degree of similarity between the two proposed approaches for healthy images (87%-98.8%)compared to those for infected images (80%-98.2%)have been obtained.Significant contribution of this study is the inclusion of the PS component on the ZC702 board which enabled the use of floating-point data representation and allowed for fast implementation with minimal resource usage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".