An Enhanced Approach for Automation the Diagnosis of Iron Deficiency Anemia Based on Quantitative Analysis of Red Blood Cells in Intestine Villi Tissue
Bibliographic record
Abstract
Recent years have witnessed a huge revolution in developing automated diagnosis for different diseases such as cancer using medical image processing. Many researchers have been conducted in this field. Analyzing medical microscopic images provide pathology medical track with large information about the status of the patients and the progress of the diseases and help in detecting any pathological changes in tissues. Automation of the diagnosis of these images will lead to a better, faster and enhanced diagnosis for different hematological and histological images. This paper proposes an automated approach for analyzing blood smear microscopic images to help in diagnosing anemia using quantitative analysis of red blood cells in intestine villi tissue. The diagnoses depends on counting the number of blue and red stained blood cells that contain iron in each villi separately, then, it calculates the percentage of blue cells and red cells in the experimented image. The experimental results have shown that using digital image processing techniques through processing the image into different stages as including noise removal, image sharpening, enhancing contrast, find region of interest, isolating color, removing edges, and counting cells leads to a successful outcome and the diagnose of anemia.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".