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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".