A Novel Regression Based Model for Detecting Anemia Using Color Microscopic Blood Images
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Bibliographic record
Abstract
Modeling human blood components and disorders is a complicated task. Few researchers have attempted to automate the process of detecting anemia in human blood. These attempts have produced satisfactory but not highly accurate results. In this paper, we present an efficient method to estimate hemoglobin value in human blood and detect anemia using microscopic color image data. We have developed a logit regression model using one thousand (1000) blood samples that were collected from Prince George Hospital laboratory. The output results of our model are compared with the results of the same sample set using CELL-DYN 3200 System in Prince George Hospital laboratory, and found to be near identical. These results exceed those reported in the literature. Moreover, the proposed method can be im-plemented in hardware with minimal circuitry and nominal cost.
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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 | 0.000 | 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 it