Automated Assessment of Erythrocyte Disorders Using Artificial Neural Network
Bibliographic record
Abstract
In this paper, we employ artificial neural network (ANN) together with image analysis techniques to automate the assessment of erythrocyte disorders using blood parameters such as red blood cell (RBC) count, hemoglobin (Hgb) level, and mean corpuscular hemoglobin (MCH). The neural network is trained using 800 blood sample images collected from the Prince George-EC, Hospital. The images are captured using a high-resolution digital camera mounted on a microscope. The red, green, and blue values of each image are fed as the input of the neural network. The Hospital RBC, Hgb values of the samples measured using hydrodynamic focused analyzer (CELL-DYN 3200 System) are provided as the target values during training. Several variations of the back propagation-learning algorithm were applied for training. The trained network is tested against 200 blood samples. The output results are compared with those of Hospital laboratory and found to be near identical, most of which are within 5% margin of error, and are much significantly better than those published. The proposed method is simple, fast, accurate, and can be a crucial step in automating laboratory reporting
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".