Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data

Adithya B. Somaraj; Buddhadev Goswami; Nirmal Punjabi; Prantar Chakrabarti; Ravindra Gudi

REVIEW 4 major objections 5 minor 25 references

Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that a pretrained ResNet-50 used for feature extraction plus an SVM classifier outperforms fine-tuning the network on blood-smear images when training data is scarce, reporting 86.75% top-1 accuracy with 168 images per…

desk verdict A useful small-data benchmark and a cautionary tale: the SVM advantage is real only at the smallest split, and the paper overstates its consistency. read the letter →

arxiv 2411.15592 v2 pith:ZAVTT2AT submitted 2024-11-23 eess.IV cs.CV

Buddhadev Goswami , Adithya B. Somaraj , Prantar Chakrabarti , Ravindra Gudi , Nirmal Punjabi This is my paper · ORCID

classification eess.IVcs.CV

keywords erythroblastnucleatedredbloodcellssmearclassificationResNet-50supportvectormachinelimitedtrainingdatatransferlearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a two-stage pipeline—a pretrained deep network used only to extract features, followed by a traditional classifier like an SVM—identifies erythroblasts in blood smear images more accurately than fine-tuning the deep network itself when labeled images are scarce. It reports that with just 168 images per class (1% of the available data), the ResNet-50+SVM version reaches 86.75% top-1 test accuracy and 98.9% erythroblast precision, against 82.03% and 98.6% for a fine-tuned ResNet-50. The authors argue this matters because high-accuracy blood-cell classification could support diagnosis of hematological disorders in resource-limited settings, where large labeled datasets are rarely available.

What carries the argument

The central object is the two-stage classification pipeline: the last pooling layer of a pretrained ResNet-50 (a deep residual network trained on natural images) produces a compact feature vector for each blood-cell image, and a support vector machine with grid-searched hyperparameters replaces the network's final classification layer. The SVM supplies a decision boundary that can be exactly tuned to the few hundred training examples, while the fixed feature extractor supplies transferable visual structure without being re-optimized on the small dataset. Training splits at 1%, 2.5%, 5%, 7.5%, 10%, 20%, and 30% of the full dataset, all evaluated against the same 4,000-image test set, make the data-scarcity comparison quantitative.

What would settle it

Re-run the 1% and 2.5% training splits while setting aside an untouched test set used only at the very end, with network architecture chosen by validation accuracy alone; if ResNet-50-SVM no longer beats the fine-tuned ResNet-50 by a clear margin, the central claim fails.

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Extended reading notes

Core claim

On a fixed 4,000-image test set from an eight-class peripheral blood cell dataset, the paper reports that a pretrained ResNet-50 used to extract features, followed by a support vector machine trained on those features, reaches top-1 accuracy of 86.75% at the 1% training split (168 images per class), against 82.03% for the same network fine-tuned end-to-end. Erythroblast precision is 98.9% for the SVM version and 98.6% for the baseline. The advantage shrinks as training data grows and is essentially gone at 30% (98.42% vs 98.36%). The central claim is that the classifier-enhanced pipeline is a data-efficient alternative for differentiating erythroblasts from white blood cells in low-data settings.

Load-bearing premise

The reported gains assume the fixed 4,000-image test set was not used to decide which network architecture to use in the first place; if that selection was influenced by the same test images, the SVM's apparent advantage at 1% data would not be out-of-sample.

Editorial extensions

If this is right

At the 1% split (168 images per class), the ResNet-50-SVM pipeline reports 86.75% top-1 test accuracy, beating the fine-tuned ResNet-50 baseline at 82.03% while keeping erythroblast precision at 98.9%.
The classifier advantage is largest at small splits and narrows as data grows; at 30% both configurations sit near 98% test accuracy.
KNN on the same features also beats the baseline at 1% and 2.5%, so the improvement is not unique to SVM.
Using a feature extractor plus a shallow classifier avoids retraining the deep network, which is computationally cheap in resource-constrained clinical settings.
The same two-stage recipe can be applied to other small medical image datasets without extra segmentation or annotation effort.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural extension, not reported in the paper, is to rerun the 1% and 2.5% comparisons across multiple random splits; this would show whether the SVM advantage is stable or depends on the single split reported.
Because the improvement is carried by a shallow classifier on deep features, the same recipe should transfer to other pretrained networks and other small medical image datasets; that is a testable extrapolation, not stated by the authors.
If the SVM advantage survives independent replication, a practical consequence is that hospitals with limited labeled data could share a fixed feature extractor and train only the cheap classifier locally, which would also reduce the amount of patient data that needs to move.
The paper does not quantify how staining variation or overlapping cells shift accuracy, so a natural follow-up is to measure the SVM advantage on data from a second site or scanner.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know about this one. The new bit is a clean benchmark of classic classifiers (SVM, KNN, RF, XGB) on deep features from ResNet-50 for eight-class blood-cell classification across training splits from 1% to 30% on the public Mendeley PBC dataset. That is genuinely useful for labs with very small labeled sets. The catch is that the paper's headline claim—'consistently surpasses'—does not survive contact with its own Table 3.

At 1% and 2.5% splits, SVM does beat the plain ResNet-50 baseline (86.75 vs 82.03 at 1%), and erythroblast precision is slightly better. But at 7.5%, 10%, and 20% the plain ResNet-50 is actually ahead on top-1 accuracy, and at 30% KNN is the best. So the support for 'consistently surpasses' is weak. The 1% gap is also a single split with no error bars, no repeated seeds, and no confidence intervals. It could be real, but the paper doesn't give you a way to tell.

The experimental protocol is under-specified. Section 4.1 selects ResNet-50 based on top-1 test accuracy on a 15% split, then Section 4.2 fixes a different 4000-image test set for all split experiments. The relationship between those two test sets isn't stated. The methodology also describes 5-fold cross-validation with mean and standard deviation, but Table 3 reports single numbers, so it's unclear whether the results are averages, one run, or something else. That needs to be cleaned up before anyone should trust the ranking.

To be fair, the paper does several things right. It uses a public dataset, links the code, reports per-class erythroblast precision/recall/F1, and is honest in the discussion about clinical limitations (overlapping cells, staining variability, cropped images). The features come from ImageNet pretraining, so the result is not forced by construction; the SVM-at-1% finding is a legitimate empirical observation worth reporting.

Who should read it: anyone doing blood-cell classification with little data, and anyone teaching experimental hygiene in ML-for-medicine. Who shouldn't: people looking for a rigorous comparison or a deployed model. I'd send it to peer review, because the question is real and the flaws are fixable; the authors need to rerun with multiple seeds, report means and intervals, and rewrite the abstract to match the data.

Referee Report

4 major / 5 minor

Summary. The paper proposes a hybrid pipeline in which a pre-trained ResNet-50 is used as a fixed feature extractor and the resulting features are fed to classical machine learning classifiers (SVM, XGBoost, KNN, Random Forest) for eight-class peripheral blood cell classification, with emphasis on erythroblast detection under small training fractions (1% to 30% of the dataset). The authors report that the ResNet-50-SVM combination consistently surpasses both plain fine-tuned ResNet-50 and the other classifier hybrids in test accuracy and erythroblast detection, and they argue this is useful for resource-constrained clinical settings. The manuscript includes a backbone comparison, a fixed 4,000-image test set, per-split accuracy and erythroblast precision/recall/F1 tables, and a discussion of limitations related to overlapping cells and staining variability.

Significance. If the central claim were established, the finding would be practically relevant: a data-efficient blood-cell classifier that maintains high accuracy and erythroblast detection with only 168 images per class could benefit low-resource hematology settings. The paper also has concrete strengths: it uses a public dataset, reports code and data availability, compares several classifiers against a fine-tuned deep baseline, and openly discusses clinical failure modes such as overlapping cells and staining variation. However, the main empirical claim rests on a single split with no uncertainty quantification and on a test set that appears to have been used for model selection, so the current evidence does not support the strength of the stated conclusion.

major comments (4)

[Abstract and Table 3] The abstract states that 'ResNet50-SVM classifier consistently surpasses other models' overall test accuracy and erythroblast detection accuracy,' but Table 3 contradicts this. Plain ResNet-50 has higher test accuracy than ResNet-50-SVM at the 7.5% split (96.29 vs. 96.07), the 10% split (96.14 vs. 96.00), and the 20% split (97.66 vs. 97.30), while at the 30% split KNN is highest (98.45 vs. 98.42 for SVM). For erythroblast detection at the 1% split, SVM has lower recall (0.812 vs. 0.857) and lower F1 (0.892 vs. 0.917) than plain ResNet-50. The evidence therefore supports at most a qualified claim about low-data test accuracy, not consistent superiority.
[Section 4.2 and Table 3] All reported results in Table 3 appear to come from a single partition of the data into one training set, one validation set, and one fixed 4,000-image test set. No error bars, repeated subsampling, multiple seeds, or statistical significance tests are provided, so the headline 4.7-percentage-point advantage of SVM over ResNet-50 at the 1% split cannot be distinguished from sampling noise. The authors should report means and standard deviations over repeated runs or, at minimum, confidence intervals for the key comparisons.
[Sections 4.1 and 4.2] The fixed 4,000-image test set appears to be used both for selecting the backbone and for evaluating the final classifiers. Section 4.1 selects ResNet-50 as the best backbone based on Table 1 'test accuracy,' and Section 4.2 then fixes the same 4,000-image testing set for all split experiments. This means no fully held-out evaluation is reported, and the reported SVM advantage may be inflated by selection on test-set performance. The authors should either reserve a separate test set untouched by any model selection or explicitly show that the conclusions are stable across multiple random splits.
[Section 4.3] The hyperparameter optimization procedure is described only by the generic equation GridSearch = arg max over accuracy, with no details of the candidate grids, the validation protocol used during grid search, or the final hyperparameter values for SVM, KNN, Random Forest, and XGBoost. If the grid search was evaluated on the same 4,000-image test set, this would be additional test-set leakage; if it was evaluated on the validation set, the authors should say so and report the chosen hyperparameters for reproducibility.

minor comments (5)

[Section 4.3] There is a typo in 'Preproccesing'; it should be 'Preprocessing'.
[Section 2] In the related work paragraph on Chola et al., 'suing' should be 'using'.
[Equation (1)] The cross-entropy loss expression lacks an explicit summation index; it should be L(y, y_hat) = -sum_i y_i log(y_hat_i) or an equivalent class-indexed sum.
[Figure 3 caption] The caption says 'Test accuracy V/S percentage of the test data,' but the x-axis appears to denote the percentage of training data used; please correct the wording.
[Section 4.2] The manuscript describes 5-fold cross-validation over the validation data but then reports results on a fixed 4,000-image test set in Table 3; the relationship between the cross-validated metrics and the reported test metrics should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports a direct empirical benchmark with externally pretrained features; no claimed prediction reduces to its inputs.

full rationale

The paper contains no derivation chain whose output is equivalent to its input. ResNet-50 features come from ImageNet pretraining, a source external to the PBC dataset, and each classifier is then trained and evaluated on fixed train/test partitions; the reported accuracies are measured outcomes, not quantities defined by the fitting procedure. The only potentially circular-adjacent issues are (i) Table 1 is used to select ResNet-50 on the basis of test accuracy before the same test-set size is fixed for the split experiments, and (ii) the abstract's "consistently surpasses" claim is not supported by Table 3 (SVM trails ResNet-50 at 7.5%, 10%, and 20% overall accuracy and trails KNN at 30%). These are evaluation and statistical weaknesses, not circularity: no fitted parameter is renamed a prediction, no self-citation is load-bearing, and no known result is repackaged as a derivation. The empirical claim is therefore self-contained and externally checkable, though the headline "consistently" overstates the tabulated numbers.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim is empirical, so no invented entities are needed. The main costs are hyperparameters tuned by grid search and domain assumptions about label quality, ImageNet transfer, and split randomness.

free parameters (4)

SVM hyperparameters (C, kernel, gamma) = not reported
Tuned by grid search (Section 4.3) for each training split. These directly determine the reported SVM accuracy.
KNN hyperparameters (number of neighbors, distance metric) = not reported
Tuned by grid search; KNN is one of the top-performing classifiers and its parameters affect the comparison.
Random Forest hyperparameters (tree count, maximum depth) = not reported
Tuned by grid search; affects the reported Random Forest results.
XGBoost hyperparameters = not reported
Tuned by grid search; affects the reported XGBoost results.

assumptions (4)

domain assumption Ground-truth labels in the Mendeley PBC dataset are correct and clinically meaningful.
All accuracy and precision numbers depend on these labels; the paper does not report re-annotation or adjudication.
domain assumption ImageNet-pretrained ResNet-50 features transfer to peripheral blood cell images without domain-specific pretraining.
The pipeline uses ImageNet weights as the feature extractor (Sections 4.2-4.3), so the entire approach relies on this transfer.
domain assumption The fixed 4,000-image test set and the random split are representative and free of leakage.
The paper does not describe patient-level or slide-level splitting; if cells from the same subject appear in both training and test sets, the numbers are optimistic.
domain assumption Validation accuracy can be used for model selection without using the test set.
The test set is reused after backbone selection in Table 1; this assumption is at risk in the reported protocol.

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Cite this review

Pith. "Pith review of Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data." pith.science (2026). https://pith.science/paper/ZAVTT2AT

@misc{pith2026241115592,
  author       = {Pith},
  title        = {Pith review of: Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZAVTT2AT}},
  note         = {Machine review of arXiv:2411.15592}
}

abstract

Hematological disorders, which involve a variety of malignant conditions and genetic diseases affecting blood formation, present significant diagnostic challenges. One such major challenge in clinical settings is differentiating Erythroblast from WBCs. Our approach evaluates the efficacy of various machine learning (ML) classifiers$\unicode{x2014}$SVM, XG-Boost, KNN, and Random Forest$\unicode{x2014}$using the ResNet-50 deep learning model as a backbone in detecting and differentiating erythroblast blood smear images across training splits of different sizes. Our findings indicate that the ResNet50-SVM classifier consistently surpasses other models' overall test accuracy and erythroblast detection accuracy, maintaining high performance even with minimal training data. Even when trained on just 1% (168 images per class for eight classes) of the complete dataset, ML classifiers such as SVM achieved a test accuracy of 86.75% and an erythroblast precision of 98.9%, compared to 82.03% and 98.6% of pre-trained ResNet-50 models without any classifiers. When limited data is available, the proposed approach outperforms traditional deep learning models, thereby offering a solution for achieving higher classification accuracy for small and unique datasets, especially in resource-scarce settings.

Figures

Figures reproduced from arXiv: 2411.15592 by the authors.

**Figure 1.** Sample images from each class of the dataset. 4 Experimental Methodology All the experiments were conducted on a single Nvidia GeForce RTX-3050 GPU device with 8 GB of RAM. We have used a batch size of 64 images, leveraging CUDA libraries for optimized performance [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Our proposed classifier enhanced ResNet-50 model architecture for the study [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Test accuracy V/S percentage of the test data for the various models. Inset shows the differences between the model for higher test data [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Correctly predicted labels of erythroblast [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Incorrect predicted labels of erythroblast 7 Conclusion This study highlights the significant impact of machine learning models in hematological diagnostics. It showcases the ability of algorithms, such as SVM and ResNet-50, to accurately classify blood cells with lim…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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