Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X

Pavan C Shekar; Shishir Maheshwari; T Sunil Kumar; Vivek kanhangad

REVIEW 3 major objections 6 minor 11 references

Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A single YOLOv8-X model both locates and classifies bleeding regions in capsule endoscopy frames, reporting 96.10% classification accuracy and 76.8% mAP at 0.5 IoU on the paper's validation split.

desk verdict A transparent, reproducible challenge baseline whose headline numbers need official test-set confirmation before they mean much. read the letter →

arxiv 2412.16624 v1 pith:EFQX6XYG submitted 2024-12-21 cs.CV cs.AI

Pavan C Shekar , Vivek Kanhangad , Shishir Maheshwari , T Sunil Kumar This is my paper · ORCID

classification cs.CVcs.AI

keywords wirelesscapsuleendoscopyGIbleedingdetectionYOLOv8-Xobjectmedicalimageclassificationdatasetcurationfalse-negativebias

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 tries to establish that a single object-detection model, YOLOv8-X, can serve as both a bleeding-region locator and a frame-level bleeder detector for wireless capsule endoscopy, eliminating the need for a separate classification network. On the authors' validation split, the model reports 96.10% classification accuracy, recall, and F1, plus 76.8% mean average precision at 0.5 IoU for bounding-box detection. The authors argue that this unified design generalizes better than separate classifiers, which reached about 99% on training data but fell to roughly 60% on external images. The practical motivation is that a real-time, single-pass model could cut the two to three hours of manual frame review needed for a capsule study down to minutes, with a deliberate bias toward flagging possible bleeding for physician review.

What carries the argument

The central object is YOLOv8-X, the extra-large variant of the YOLOv8 single-stage object detector, whose backbone extracts multi-scale image features, neck aggregates them, and head outputs class scores and bounding boxes at different strides. The unifying trick is that the detection head's class score doubles as the frame-level classification: if any box is predicted with a bleeding class, the frame is flagged, so localization and classification share one training signal. Alongside the architecture, the paper's engineered data pipeline—expanding the initial 2,618 frames to 6,345 images, fully reannotating them, and using an 80/20 split chosen to span bleeding presentations—carries much of the argument, since the authors found each annotation refinement measurably improved results.

What would settle it

Run the released model on the official Auto-WCEBleedGen test dataset (the one cited as [3] in the paper), or perform a three-fold cross-validation where one fold is never used for any curation decision, and compare the resulting classification accuracy and mAP with 96.10% and 76.8%. If the held-out numbers are materially lower, the reported figures reflect validation-driven curation rather than pure generalization.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that careful dataset expansion and reannotation, combined with an extra-large YOLOv8 single-stage detector, yields a system that both draws bounding boxes around bleeding regions and classifies frames as bleeding or non-bleeding in one forward pass. The reported performance—96.10% for accuracy, recall, and F1, and 76.8% mAP at 0.5 IoU with an average IoU of 80.75%—is presented as evidence that the unified model is clinically usable without sacrificing sensitivity. The authors also report that the model was intentionally tuned to minimize false negatives, accepting extra false positives as the safer trade-off for screening.

Load-bearing premise

The load-bearing premise is that the 80/20 validation split is an independent, representative sample of unseen bleeding frames, even though the authors repeatedly used its metrics to guide dataset and annotation refinement.

Editorial extensions

If this is right

If the model's validation performance carries over to new patients, a single YOLOv8-X pass can replace the two-stage detection-plus-classification setups that dominate current practice.
A high-recall system with a known false-positive bias is best deployed as a screening filter that flags suspect frames for a human reviewer, not as a final diagnostician.
The gains the authors attribute to dataset curation imply that investing in high-quality bounding-box annotations for medical images can matter more than swapping model architectures.
Because the model processes frames independently, the same pipeline can be run on the full set of 60,000 to 100,000 frames in a capsule study in near real time.

Reading between the lines

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

Not claimed by the paper: because the validation set was used repeatedly to steer dataset and annotation refinements, the reported numbers are best read as an upper bound on generalization until a truly held-out or external split is reported.
A natural extension not tested here: temporal context across consecutive capsule frames could reduce the false positives that the paper's sensitivity-first tuning accepts, since the model currently analyzes each frame independently.
The same recipe—start with whatever public data exists, reannotate carefully, then expand—could plausibly transfer to other endoscopy findings such as polyps or angiodysplasia, though the paper does not test this.
If clinicians want more than a box, extending the detection head to a segmentation head is an obvious next step and would give the detailed bleeding boundaries the paper lists as future work.

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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

Quick take: this is a workmanlike challenge write-up, not a research advance. The genuinely useful parts are the released code and the transparent account of dataset curation. The soft underbelly is the evaluation: the headline numbers come from a self-curated validation split that the authors used to guide data and annotation refinements, so they do not yet establish generalization.

What's new: a single YOLOv8-X model handles both detection and classification on WCE frames, trained on an expanded dataset of 6,345 images. There's no new architecture or scientific insight; the contribution is a reproducible baseline and a data-cleaning pipeline. Credit where due: the paper is honestly written, describes the iterative data-improvement loop explicitly, and ships code and trained models. That puts it ahead of many challenge reports that leave out implementation details.

Soft spots, in order of importance. First, the validation split is not an independent test set. Section 2.1 says the 80/20 split was 'strategically designed' to cover the full spectrum of bleeding presentations, and Section 2.3 says each data enhancement was followed by a new training cycle measuring impact on validation metrics. That's a recipe for silent overfitting to the validation distribution. The paper shows only qualitative results on the official Auto-WCEBleedGen test datasets, even though quantitative numbers should be available. This is the main load-bearing weakness. Second, the identical 96.10% for accuracy, recall, and F1 looks suspicious and is not explained; there are no confidence intervals, no confusion matrix, and no class-balance information. That's a minor reporting gap but easy to fix. Third, hyperparameters are described only qualitatively (batch sizes 16 and 32, cosine decay); enough to reproduce roughly, but not exactly.

These are all fixable. I don't see anything fundamentally wrong with the method, and the claim that data quality drove performance is credible. The paper's own limitation section acknowledges the small dataset and suggests video-level analysis, which is a reasonable next step.

Who should read it: applied medical-imaging researchers wanting a baseline or a worked example of dataset curation; challenge organizers benchmarking submissions. It is not a methods paper.

Recommendation: it deserves a serious referee, not a desk reject, but I'd send it back for major revision with a hard requirement for official test-set metrics and a clear statement of how the validation split was created. My own confidence in the 96.10% number is moderate until then.

Referee Report

3 major / 6 minor

Summary. The paper describes a solution to the Auto-WCEBleedGen V1 Challenge using a unified YOLOv8-X model for simultaneous detection and classification of bleeding regions in wireless capsule endoscopy (WCE) images. The authors expand a base challenge dataset from 2,618 to 6,345 images using additional public WCE data and frame extraction, reannotate the entire dataset through iterative refinement, and train YOLOv8-X on an 80/20 train/validation split. On this validation split they report 96.10% classification accuracy, recall, and F1, and 76.8% mAP@0.5 for detection. The paper includes qualitative detection examples on the official test datasets but no quantitative test-set results. Code and trained models are publicly available.

Significance. If the reported metrics hold on truly independent data, this is a useful deployable baseline for automated WCE bleeding review, and the authors deserve credit for releasing code, weights, and a clearly documented dataset-expansion and reannotation effort. The emphasis on high recall aligns with clinical safety priorities. However, the headline numbers are computed on a self-curated validation split that was iteratively used to guide dataset and annotation refinement, so they cannot currently be read as generalization estimates. Because official held-out test datasets exist and were used qualitatively in the paper, an independent quantitative evaluation is feasible and would substantially strengthen the contribution. As presented, the paper is a solid engineering report but does not yet establish the central generalization claim.

major comments (3)

[Sec. 2.1 and 2.3] The central claim of 96.10% accuracy and 76.8% mAP@0.5 is computed on a validation split whose independence from the development loop is not demonstrated. Section 2.1 states that the 80/20 split was 'strategically designed' to cover the full spectrum of bleeding presentations, and Section 2.3 describes an iterative process in which each data and annotation enhancement was followed by a training cycle and measurement of validation metrics. This protocol can inflate validation numbers relative to unseen data. The authors should report quantitative results on the official Auto-WCEBleedGen test datasets (Section 6, reference [3]) — for example accuracy, recall, F1, and mAP — since those sets are held out with respect to the training data used here.
[Sec. 3.1, Table 1] Accuracy, recall, and F1 are all reported as exactly 96.10% to two decimal places, which is mathematically possible only under specific class distributions or rounding, and the paper gives no supporting counts. Without a confusion matrix or per-class numbers, the classification result cannot be audited or reproduced by the reader. Please report the confusion matrix, the number of validation images per class, and per-class precision and recall.
[Sec. 2.1] The paper does not state whether the 80/20 split is performed at the patient/video level or at the frame level. If frames from the same video sequence appear in both training and validation, the validation metrics will be optimistically biased for the clinical use case of reviewing new patients. Please clarify the split unit, and if the current split is frame-based, provide an additional patient- or video-level split evaluation to support the generalization claim.

minor comments (6)

[Sec. 2.3] The final training configuration is not reported: the number of epochs, batch size, learning rate schedule, input image size, and augmentation settings used for the final YOLOv8-X model are missing. Please include these values or provide a pointer to the configuration file in the public repository.
[Sec. 3.1, Table 1] The exact definitions of 'Average Precision' and 'Intersection over Union' in this table are unclear: state whether AP is per-class or averaged, how mAP@0.5 is computed, and how the 80.75% IoU figure is derived (for example, mean per-image IoU).
[Figures 3 and 4] The qualitative results on the official test datasets are not accompanied by any quantitative evaluation on those sets, nor by a description of how the displayed examples were selected; please add this information to avoid cherry-picking concerns.
[Sec. 3.4] The claim that 'clinicians have indicated that this trade-off is appropriate' is unsupported: no citation, consultation procedure, or expert count is provided. Please add support or soften the claim.
[References] Reference [5] is cited to support a 'MATLAB-based MobileNet model', but it points to the original MobileNets paper; please add the correct MATLAB implementation reference or remove the qualifier.
[Sec. 2.2] The classification task is not precisely defined; please state the number of classes and the label space (for example, binary bleeding versus non-bleeding, or multi-class bleeding types) used for the reported 96.10% classification accuracy.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the reported metrics are empirical validation measurements, not quantities derived from or equivalent to the model's inputs by construction.

full rationale

This paper makes an empirical performance claim for a standard YOLOv8-X model on a self-curated dataset. There is no derivation chain in which a predicted quantity is identical to an input by construction, no fitted parameter that is later renamed as a prediction, and no load-bearing self-citation. The model architecture and training framework come from external sources (YOLOv8, Ultralytics), and the challenge dataset references are external. The validation set was indeed used iteratively to guide dataset and annotation refinements, and the final numbers are reported on that same validation split. This is a real methodological concern about generalization and could overstate performance, but it is not circularity under the defined patterns: the accuracy and mAP values are measured outcomes, not mathematical consequences of the curation decisions. No equation or definitional equivalence is offered that would make the headline result equal to its inputs. Therefore the circularity score is 0, and the validation-leak risk should be recorded as a correctness or evaluation-design limitation rather than as circularity.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

No unproven derived entities are introduced. The results rest on dataset composition, annotation quality, and hyperparameter choices, all of which are partially unspecified.

free parameters (3)

Detection confidence threshold = not reported
Tuned to favor sensitivity over precision as described in Sec. 3.3; the exact threshold is not reported.
Training hyperparameters (batch size, learning rate schedule, epochs) = batch 16/32, cosine decay, epochs not stated
Chosen empirically during development (Sec. 2.3); final exact values are not fully specified.
Train/validation split ratio = 80/20
Chosen by hand so the validation set covers the spectrum of bleeding presentations (Sec. 2.1).

assumptions (2)

domain assumption The 6,345-image curated dataset, including the 2,618 challenge frames and additional external frames, is labeled with accurate bounding boxes and class labels.
The entire performance evaluation relies on annotation correctness (Sec. 2.1).
domain assumption The validation split is representative of WCE bleeding presentations and independent of the training split.
Claimed in Sec. 2.1; if false, the reported metrics are biased.

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Cite this review

Pith. "Pith review of Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X." pith.science (2026). https://pith.science/paper/EFQX6XYG

@misc{pith2026241216624,
  author       = {Pith},
  title        = {Pith review of: Automated Bleeding Detection and Classification in Wireless Capsule Endoscopy with YOLOv8-X},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EFQX6XYG}},
  note         = {Machine review of arXiv:2412.16624}
}

read the original abstract

Gastrointestinal (GI) bleeding, a critical indicator of digestive system disorders, re quires efficient and accurate detection methods. This paper presents our solution to the Auto-WCEBleedGen Version V1 Challenge, where we achieved the consolation position. We developed a unified YOLOv8-X model for both detection and classification of bleeding regions in Wireless Capsule Endoscopy (WCE) images. Our approach achieved 96.10% classification accuracy and 76.8% mean Average Precision (mAP) at 0.5 IoU on the val idation dataset. Through careful dataset curation and annotation, we assembled and trained on 6,345 diverse images to ensure robust model performance. Our implementa tion code and trained models are publicly available at https://github.com/pavan98765/Auto-WCEBleedGen.

Figures

Figures reproduced from arXiv: 2412.16624 by the authors.

**Figure 2.** Examples of successful bleeding detection from our validation dataset, showing the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Detection results on Test Dataset 1 showing three different bleeding cases with [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Detection results on Test Dataset 2 showing bleeding regions (marked by white [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

11 extracted references · 4 canonical work pages

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Palak Handa, Nishu Pandey, Divyansh Nautiyal, Nidhi Goel, and Deepak Gunjan. Autowcebleedgen-test dataset (improved), February 2024. URL https://doi.org/10. 5281/zenodo.10642779

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [3]

Autowcebleedgen-test dataset (improved), February 2024

Palak Handa, Nishu Pandey, Divyansh Nautiyal, Nidhi Goel, and Deepak Gunjan. Autowcebleedgen-test dataset (improved), February 2024. URL https://doi.org/10. 5281/zenodo.10642779

work page 2024

[2] [1]

End-to-end object detection with transformers

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision – ECCV 2020, pages 213–229, Cham, 2020. Springer International Publishing. ISBN 978-3-030- 58452-8

work page 2020

[3] [2]

Auto-wcebleedgen version v1 and v2: Challenge, datasets and evaluation

Palak Handa, Divyansh Nautiyal, Deepti Chhabra, Manas Dhir, Anushka Saini, Shreshtha Jha, Harshita Mangotra, Nishu Pandey, Advika Thakur, et al. Auto-wcebleedgen version v1 and v2: Challenge, datasets and evaluation. Authorea Preprints, 2024. doi: 10.22541/ essoar.171007121.19572474/v1

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Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016. doi: 10.1109/CVPR.2016.90

doi:10.1109/cvpr.2016.90 2016

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Mobilenets: Efficient convolutional neural networks for mobile vision applications

Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. In arXiv preprint arXiv:1704.04861, 2017

arXiv 2017

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Rtmdet: An empirical study of designing real-time object detec- tors

Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, and Kai Chen. Rtmdet: An empirical study of designing real-time object detec- tors. ArXiv, abs/2212.07784, 2022. URL https://api.semanticscholar.org/CorpusID: 254685870

arXiv 2022

[7] [7]

Wce- bleedgen: A wireless capsule endoscopy dataset containing bleeding and non-bleeding frames, November 2023

Palak, Harshita Mangotra, Divyansh Nautiyal, Jyoti Dhatarwal, and Nidhi Gooel. Wce- bleedgen: A wireless capsule endoscopy dataset containing bleeding and non-bleeding frames, November 2023. URL https://doi.org/10.5281/zenodo.10156571

work page doi:10.5281/zenodo.10156571 2023

[8] [8]

Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection

Konstantin Pogorelov, Kristin Ranheim Randel, Carsten Griwodz, Thomas de Lange, Dag Johansen, Concetto Spampinato, Duc-Tien Dang-Nguyen, Mathias Lux, Peter Thelin Schmidt, and Michael Riegler. Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM on Multimedia Systems Conference, pages 164–169, 2017

work page 2017