A Comparative Analysis of ResNet-Based White Blood Cell Classification Across Multi-Scale Datasets for Enhanced Hematological Diagnostics
Bibliographic record
Abstract
In this proposed research work, we used the power of deep learning models, ResNetwork to correctly explore WBC. For implementation, six different datasets are collected. In Each datasets images are clicked from different angle and also a combined dataset is made. Some challenges are noticed while creating a new dataset of all the images. The disturbances in the images like overlapping cells and artifacts. ResNet can train the deep layers correctly, it lowers the problem of vanishing gradient. Combined data can enhance the generalization but the performance can be degraded due to versatility. Some classes work good on individual dataset but some typos are shown in rare classes like basophils or blasts. Domain adaptation, data augmentation and explainable AI are the possible suggestions to improve the model. A model alone is not enough for accurate WBC classification. Data variability, class imbalance, and interpretablity must also be handled. Only then can AI-based diagnostics become reliable and scalable.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".