Automatic White Blood Cell Classification Using Convolutional Neural Network
Bibliographic record
Abstract
Peripheral blood smear analysis is a commonly used technique for existing abnormalities in the health status of humans. Disorders in White Blood Cell (WBC) ratio imply the existence of diseases such as leukemia, lymphoma, anemia, and myelodysplastic syndrome (MDS). Deep learning techniques have gained promising results in many medical-related tasks such as dermatology, ophthalmology, and gastroenterology. In this paper, we developed a novel Convolutional Neural Network (CNN) architecture for leukocyte classification from microscopic peripheral blood cell images which help in the diagnosis of hematological disorders. To boost the performance of the proposed model and avoid overfitting phenomena we leveraged data augmentation techniques of rotation, flipping, width shift, and height shift. Unlike the conventional practice for designing a custom CNN model, we stacked homogenous layers and larger kernels in the early layers. To evaluate the performance of the proposed model, we have used classification metrics of accuracy F1- scores. The proposed model achieved an accuracy of 98.13% on the LISC dataset and then benchmarked against the state-of-the-art approaches.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".