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REVIEW 4 major objections 5 minor 35 references

Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A fine-tuned Inception-ResNet-v2 distinguishes acute lymphoblastic leukemia from normal blood cells with 99.7% accuracy.

desk verdict A plausible binary ALL/normal benchmark whose stage and hematogone claims are impossible by construction; worth a referee only to force an honest reframing. read the letter →

arxiv 2502.09804 v1 pith:4FKHSE5V submitted 2025-02-13 eess.IV cs.AIcs.CVcs.LG

Alaa Awad , Salah A. Aly This is my paper · ORCID
classification eess.IVcs.AIcs.CVcs.LG
keywords AcuteLymphoblasticLeukemiaYOLOv11YOLOv8ResNet50Inception-ResNet-v2imagesegmentationhematogonestransferlearning
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

This paper claims that deep learning models fine-tuned on multi-cell blood-smear images can tell malignant white blood cells from healthy ones reliably enough for real-world leukemia screening. The authors merge two public multi-cell datasets, the ALL image dataset and ALL-IDB1, into Normal and Cancer classes, so the models cannot rely on single-cell crops. The reported results put Inception-ResNet-v2 at 99.7% accuracy with 100% specificity, with YOLOv11, YOLOv8, and ResNet50 in the 97–99% range. A sympathetic reader would care because the method is aimed at early stages of ALL and at hematogones, the benign lookalike cells that often cause false ALL diagnoses.

What carries the argument

The argument is carried by a two-stage pipeline. In the first stage, blood-smear images are converted to HSV color space (hue, saturation, value), and a threshold on the purple hue of white blood cells builds a binary mask that removes backgrounds, platelets, and red cells; the exact threshold values are not reported. In the second stage, pretrained YOLOv8, YOLOv11, ResNet50, and Inception-ResNet-v2 are fine-tuned on the masked images, with the two ResNet-family classifiers having their last ten layers unfrozen and using SGD at a 0.001 learning rate with augmentation such as flipping, rotation, zoom, and mosaic. This transfer-learning step is what lets a modest merged dataset reach the reported accuracies above 97%.

What would settle it

Reproduce the segmentation using the exact HSV thresholds, which the paper does not report, on blood-smear images from a third laboratory; if the binary masks lose parts of blast nuclei or retain platelet fragments, then the near-perfect test scores are bound to the two source datasets rather than to a generalizable rule.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that fine-tuning large convolutional models on multi-cell blood-smear images yields near-perfect binary ALL classification. Inception-ResNet-v2 records 99.7% test accuracy with 100% precision and 100% specificity, and ResNet50 records 99% accuracy with 100% recall; the YOLO detectors trail slightly, with YOLOv11s at 98.2% accuracy and YOLOv11n at 97.3%. Because the Normal class contains benign cells and hematogones while the Cancer class contains early, pre, and pro stages, the authors read these metrics as evidence that the models recognize early-stage disease and resist the standard hematogone false-positive trap.

Load-bearing premise

The result rests on a fixed set of hand-picked color thresholds that separate white blood cells from everything else in every image; if those thresholds are sensitive to staining, lighting, or dataset origin, the near-perfect accuracy will not hold on new slides.

Editorial extensions

If this is right

  • A blood-smear slide can be classified as malignant or benign in a single pass, because the models were trained and tested on multi-cell images rather than isolated cells.
  • Early, pre, and pro stages are folded into the Cancer class, so a correct positive prediction can in principle catch ALL before blast cells dominate a sample.
  • Hematogones are treated as Normal, and the 100% specificity of Inception-ResNet-v2 on the test set means these lookalike cells produce almost no false alarms.
  • Because the merged dataset combines images from two sources, the reported accuracies support the authors' claim that the method is not tied to a single imaging setup.

Reading between the lines

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

  • A natural stress test is to run the same unreported HSV thresholds on blood-smear images from a third laboratory; the outcome would show whether the 99.7% figure transfers across staining and illumination conditions.
  • The per-model trade-offs are uneven, so a deployed system would likely pair a high-specificity classifier like Inception-ResNet-v2 with a high-recall classifier like ResNet50 rather than rely on one model.
  • A further testable extension is to convert the multi-cell binary decision into cell-level counts, using the number of blast cells a model misses per slide to set a clinical alert threshold.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper proposes a binary classification pipeline for Acute Lymphoblastic Leukemia (ALL) detection using YOLOv11n, YOLOv11s, YOLOv8s, ResNet50, and Inception-ResNet-v2. The authors merge two multi-cell datasets (ALL image dataset and ALL-IDB1), segment white blood cells with HSV thresholding, and train with transfer learning and data augmentation. They report test accuracies ranging from 97.3% (YOLOv11n) to 99.7% (Inception-ResNet-v2). The abstract and contributions additionally claim that the models can identify different ALL stages, including early stages, and can detect hematogones.

Significance. If the binary results are reproducible, the paper would provide a useful comparison of several deep-learning architectures on a multi-cell ALL detection task, a more realistic setting than single-cell benchmarks. The reported accuracies are plausible and the integration of two datasets is a sensible step toward robustness. However, the headline claims about stage identification and hematogone detection are not supported by the described experimental design, and the absence of segmentation threshold values and split details prevents replication. The contribution is therefore substantially weaker than the abstract suggests, though the underlying empirical comparison may still be of some value.

major comments (4)
  1. [§II (Table III), Abstract, Contributions] The central claims that the models "can identify different ALL stages, including early stages" and "can detect hematogones" are contradicted by the paper's own experimental design. Section II states that the merged dataset collapses Early, Pre, Pro, and Cancer into a single "Cancer" class, and Benign (including Hematogones) together with ALL-IDB1 Normal into a single "Normal" class. All reported confusion matrices (Figs. 8, 15, 22, 25, 28) are binary. A binary classifier has no output units for stage labels or for a separate hematogone class, so these claims are impossible given the described architecture and labels. The authors must either remove these claims or design a multi-class experiment with separate labels for each stage and for hematogones.
  2. [§III-A] The image segmentation step depends on hand-set HSV thresholds, but the threshold values are never reported. The text says only that "upper and lower thresholds were set for the purple hue of the white blood cells." Without these values, the preprocessing is not reproducible, and the claim that the results generalize to "real-world scenarios" is unverifiable. The authors should provide the exact threshold values and discuss their sensitivity to staining, illumination, and dataset origin.
  3. [§III-A] The description of the train/validation/test split (70/15/15) is incomplete. The authors do not state whether the split was done at the image level, whether stratification was applied across the two merged datasets, or whether images from the same patient or source could appear in multiple splits. If image-level leakage occurred, the reported accuracies could be inflated. Specifics on the split procedure are needed to assess the validity of the performance numbers.
  4. [§V–§VIII and Table IV] All accuracy, F1, precision, recall, and specificity values are reported as point estimates from a single training run, with no error bars, confidence intervals, or multiple seeds. Given that ALL-IDB1 contributes only 108 images and the test set is a 15% subset of the merged data, the observed differences between models (e.g., 97.3% vs. 99.7%) may not be statistically significant. The paper should include repeated runs or at least confidence intervals to support comparative claims.
minor comments (5)
  1. [§III-A] The sentence "This mask was then applied to the original images ??, allowing the segmentation of the white blood cells" contains a stray "??" placeholder and is grammatically incomplete; it should be corrected.
  2. [§V] In the comparison of YOLOv11s and YOLOv11n, the text states the improvement is "0.9" without specifying units; it should read "0.9 percentage points" to avoid ambiguity.
  3. [§XI] The conclusion refers to "YOLO11vs" and "YOLO8vs," which are inconsistent with the model names "YOLOv11s" and "YOLOv8s" used elsewhere in the paper.
  4. [§V–§VIII] Several figure captions are generic (e.g., "Training and Validation Accuracy") and do not clearly identify which model and configuration the figure corresponds to; this makes it harder to map the results to the text.
  5. [§X (Table V)] The comparison in Table V mixes results from different datasets (C-NMC 2019, ALL-IDB1, ALL dataset) without noting that direct accuracy comparison across datasets is not meaningful; a clarifying statement is needed.

Circularity Check

2 steps flagged · score 6.0 of 10

Stage and hematogone claims collapse into Normal/Cancer labels by construction; the reported accuracies themselves are empirical and not circular.

  1. self definitional [Abstract; Section II (Table III)]
    "Abstract: "the proposed models can identify different ALL stages, including early stages." Section II: "we combined the samples from the Early, Pre and Pro classes from the ALL image dataset with the Cancer class samples from the ALL-IDB1 dataset into one class called Cancer.""

    The Cancer training target is defined as the union of Early, Pre, Pro, and ALL-IDB1 Cancer samples, while the output layer and all confusion matrices are binary Normal/Cancer. A correct Cancer prediction for an early-stage image is therefore just the merged Cancer label by construction; the model never sees stage labels and cannot output a distinct stage. The claimed stage identification reproduces the collapsed label and is not derived from any stage-level supervision.

  2. renaming known result [Introduction (contribution 4); Section II (Table III)]
    "Introduction: "The models demonstrate the capability to detect hematogones cases, despite their frequent misclassification as ALL." Section II: "we merged the normal cells from the ALL-IDB1 dataset with the benign cells from the ALL image dataset under one category called Normal.""

    Hematogones are described as a subset of the Benign class, and Benign is collapsed with ALL-IDB1 Normal into a single Normal class. The model is trained and evaluated only on Normal versus Cancer, with no hematogone-specific labels or outputs. Reporting Normal predictions for benign cells as hematogone detection is a rename of the binary Normal/Cancer accuracy, not an evaluation against hematogone ground truth.

full rationale

The headline accuracies (97.3% to 99.7%) are empirical measurements on a 70/15/15 split with standard fine-tuning, so they are not circular: no fitted parameter is renamed as a prediction, and no self-citation supplies the result. The circularity is confined to the abstract, introduction, and conclusion claims that the binary models 'identify different ALL stages' and 'detect hematogones.' Because Table III removes stage and hematogone information by merging all malignant subtypes into Cancer and all benign/normal (including hematogones) into Normal, those two advertised capabilities are equivalent, by construction, to the collapsed binary labels rather than to separate outputs. The only self-citation ([11]) is used for earlier YOLOv8/YOLOv11 results and Table V; the current models and metrics are generated in this paper, so that citation is not load-bearing. No other circular step was found.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new entities are introduced. The central claims rest on tuned preprocessing and hyperparameters plus assumptions about label fidelity and transfer learning. The stage and hematogone claims are artifacts of label merging rather than independent model outputs.

free parameters (6)
  • HSV segmentation thresholds = Not reported
    Manual thresholds in HSV space separate purple WBC nuclei from background and platelets; the values directly control all downstream inputs.
  • Learning rate = 0.001
    Selected after experiments; final value is a tuned constant affecting convergence and final accuracy.
  • Batch size = 8 for YOLOv8, ResNet50, and Inception-ResNet-v2; 32 for YOLOv11
    Batch sizes of 8, 16, 32, and 64 were compared, and the best-performing values were chosen.
  • Number of unfrozen layers = 10
    Unfreezing the last 10 layers for ResNet50 and Inception-ResNet-v2; other counts gave lower accuracy.
  • Number of epochs = 50
    Chosen because 100 or more epochs caused overfitting and performance drops.
  • Augmentation parameters = Mosaic probability 1.0, flip probability 0.5, rotation up to 45 degrees, scale 0.5
    Set by hand and directly shape the training distribution.
assumptions (5)
  • domain assumption Class labels in ALL image dataset and ALL-IDB1 are correct ground truth.
    The merged labels in Table III assume that the public datasets' benign, cancer, and stage annotations are accurate.
  • domain assumption HSV thresholding isolates white blood cells without discarding diagnostic information.
    The segmentation step removes backgrounds and platelets, but no validation is provided that it does not remove blast cells or relevant nuclei.
  • domain assumption ImageNet-pretrained weights transfer to blood smear images.
    Transfer learning from ImageNet to medical microscopy is standard practice but is not validated for this specific domain.
  • domain assumption The 70/15/15 train/validation/test split is independent and contains no leakage.
    The paper does not specify subject-level or image-level splitting, so duplicates or near-duplicates could inflate accuracy.
  • ad hoc to paper In-distribution test accuracy reflects real-world multi-cell scenarios.
    The abstract claims real-world relevance, but only two public datasets with limited staining and imaging conditions are used.

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Cite this review

Pith. "Pith review of Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models." pith.science (2026). https://pith.science/paper/4FKHSE5V

@misc{pith2026250209804,
  author       = {Pith},
  title        = {Pith review of: Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4FKHSE5V}},
  note         = {Machine review of arXiv:2502.09804}
}
read the original abstract

Thousands of individuals succumb annually to leukemia alone. As artificial intelligence-driven technologies continue to evolve and advance, the question of their applicability and reliability remains unresolved. This study aims to utilize image processing and deep learning methodologies to achieve state-of-the-art results for the detection of Acute Lymphoblastic Leukemia (ALL) using data that best represents real-world scenarios. ALL is one of several types of blood cancer, and it is an aggressive form of leukemia. In this investigation, we examine the most recent advancements in ALL detection, as well as the latest iteration of the YOLO series and its performance. We address the question of whether white blood cells are malignant or benign. Additionally, the proposed models can identify different ALL stages, including early stages. Furthermore, these models can detect hematogones despite their frequent misclassification as ALL. By utilizing advanced deep learning models, namely, YOLOv8, YOLOv11, ResNet50 and Inception-ResNet-v2, the study achieves accuracy rates as high as 99.7%, demonstrating the effectiveness of these algorithms across multiple datasets and various real-world situations.

Figures

Figures reproduced from arXiv: 2502.09804 by the authors.

Figure 1
Figure 1. Workflow Diagram This study employs image processing techniques, such as segmentation, to prepare the dataset. Additionally, it utilizes transfer learning and fine-tuning methodologies on YOLOv11 [8], YOLOv8 [9], and ResNet50 [10], achieving results ranging from 97% to 99%, see our initial results [11]. The contributions of this study can be summarized as follows: 1) Up to our knowledge, this is the first research c… view at source ↗
Figure 2
Figure 2. The implementation process A. Dataset Preparation The dataset was preprocessed using image processing tech￾niques to remove redundant elements and improve the models’ performance. First, unnecessary elements, such as varying backgrounds and unrelated blood components like platelets, were removed from the images. This step was essential to enable the model to focus on the white blood cells, which [PITH_FULL_IMAGE:fi… view at source ↗
Figure 3
Figure 3. Data samples before and after image segmentation. (a) Before [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (18 more)
Figure 5
Figure 5. Figure 5: YOLOv11n accuracy with SGD [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: YOLOv11n train loss with SGD [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: YOLOv11n validation loss with SGD [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 9
Figure 9. Figure 9: YOLOv11n accuracy with AdamW [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 8
Figure 8. Figure 8: Normalized confusion matrix for YOLOv11n with SGD. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 10
Figure 10. Figure 10: YOLOv11n train loss with AdamW [PITH_FULL_IMAGE:figures/full_fig_p006_10.png]
Figure 11
Figure 11. Figure 11: YOLOv11n validation loss with AdamW [PITH_FULL_IMAGE:figures/full_fig_p006_11.png]
Figure 14
Figure 14. Figure 14: YOLOv11s validation loss with SGD [PITH_FULL_IMAGE:figures/full_fig_p007_14.png]
Figure 13
Figure 13. Figure 13: YOLOv11s train loss with SGD There was a slight improvement in the confusion matrix as well, which can be illustrated in [PITH_FULL_IMAGE:figures/full_fig_p007_13.png]
Figure 16
Figure 16. Figure 16: YOLOv11s accuracy with AdamW [PITH_FULL_IMAGE:figures/full_fig_p007_16.png]
Figure 17
Figure 17. Figure 17: YOLOv11s train loss with AdamW [PITH_FULL_IMAGE:figures/full_fig_p008_17.png]
Figure 18
Figure 18. Figure 18: YOLOv11s validation loss with AdamW VI. YOLOV8S PERFORMANCE RESULTS The visual representation of YOLOv8 behavior is shown in figures 19, 20, and 21, in which the accuracy on the validation dataset was 96.6%, and it peaked at 98% when evaluated on the testing dataset. …
Figure 21
Figure 21. Figure 21: YOLOv8s validation loss with SGD [PITH_FULL_IMAGE:figures/full_fig_p008_21.png]
Figure 22
Figure 22. Figure 22: Confusion matrix for YOLOv8s with SGD accuracy grew to 99%, and the test dataset evaluation achieved 99%. The visualization of the training and validation accuracy curves in [PITH_FULL_IMAGE:figures/full_fig_p009_22.png]
Figure 23
Figure 23. Figure 23: Training and Validation Accuracy [PITH_FULL_IMAGE:figures/full_fig_p009_23.png]
Figure 26
Figure 26. Figure 26: Training and Validation Accuracy The confusion matrix of the model is visualized in [PITH_FULL_IMAGE:figures/full_fig_p009_26.png]
Figure 27
Figure 27. Figure 27: Training and Validation Loss [PITH_FULL_IMAGE:figures/full_fig_p010_27.png]
Figure 28
Figure 28. Figure 28: Confusion Matrix for Inception-ResNet-v2 [PITH_FULL_IMAGE:figures/full_fig_p010_28.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.