Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays

Abdesselam Ferdi

REVIEW 4 major objections 5 minor 21 references

Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read G-YOLOv11, a YOLOv11 variant with ghost convolutions and halved filter counts, reports wrist-fracture mAP@0.5 of 0.535 at 2.4 ms inference on an A10 GPU, reducing model size by 68.7% versus YOLOv11l.

desk verdict A clean but conceptually derivative lightweight YOLOv11 for wrist fractures; the accuracy numbers are unreliable because the data split leaks patients. read the letter →

arxiv 2501.00647 v1 pith:6MEWRDL2 submitted 2024-12-31 eess.IV cs.CV

Abdesselam Ferdi This is my paper · ORCID

classification eess.IVcs.CV

keywords pediatricwristfracturedetectionYOLOv11ghostconvolutionlightweightobjectcomputer-aideddiagnosisX-rayimagingGRAZPEDWRI-DXmodelcompression

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 show that a pediatric wrist fracture detector can become much cheaper to run without giving up too much accuracy. The proposed detector, G-YOLOv11, takes YOLOv11 and replaces ordinary convolutions and C3k2 blocks with ghost convolution and C3Ghost blocks, and it halves the number of filters in each stage. On the GRAZPEDWRI-DX wrist X-ray dataset, the large variant reports an mAP@0.5 of 0.535 with a 2.4 ms inference time on an NVIDIA A10 GPU, using 7.8 million parameters and 21.0 GFLOPs. Compared with the standard large YOLOv11, this is a 13.6% relative drop in mAP@0.5 while reducing model size by 68.7%. The authors claim this establishes a new efficiency benchmark, arguing that modest accuracy loss is an acceptable trade for real-time clinical use.

What carries the argument

The load-bearing mechanism is ghost convolution, which produces the same number of feature maps as a standard convolution by first computing a small set of intrinsic feature maps with a 1x1 convolution and then generating the remaining maps with cheap 5x5 linear operations, concatenating both outputs. The paper inserts this operation in place of every Conv module and packages it into C3Ghost blocks, which combine two ghost convolutions around a depthwise convolution with a shortcut path, replacing YOLOv11's C3k2 blocks. On top of this substitution, each stage's filter count is halved relative to the corresponding YOLOv11 variant. These two changes together cut parameters, FLOPs, memory, and inference time, and they carry the efficiency-accuracy trade-off reported in the paper's experimental tables.

What would settle it

Retrain G-YOLOv11l with the same hyperparameters but on a patient-disjoint split of GRAZPEDWRI-DX, meaning all images from a given patient are confined to a single fold, and compare mAP@0.5 on the held-out patients; if the score falls well below the reported 0.535, the published accuracy is inflated by images of the same patients appearing in both training and test sets.

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Extended reading notes

Core claim

The paper's central claim is that the standard YOLOv11 detector, which is too heavy for clinical computer-aided diagnosis, can be turned into a lightweight detector by replacing its convolution and C3k2 modules with ghost convolution and C3Ghost modules and halving the filter counts across the network. On the GRAZPEDWRI-DX test set, G-YOLOv11l reaches an mAP@0.5 of 0.535 and an mAP@0.5:0.95 of 0.341 with an inference time of 2.4 ms on an NVIDIA A10 GPU, using 7.794 million parameters and 21.0 GFLOPs. In contrast, YOLOv11l achieves 0.619 mAP@0.5, 25.317 million parameters, 87.3 GFLOPs, and 6 ms inference, so the proposed model trades a 13.6% relative accuracy reduction for a 68.7% smaller model and roughly 2.5x faster inference. The paper interprets this as a new efficiency-oriented benchmark, because it outperforms existing detectors on resource usage while remaining within a moderate accuracy range.

Load-bearing premise

The data split is a random 70/20/10 split of individual images, not of patients, so images from the same child can appear in both training and test sets; if that happens, the reported mAP values are inflated and the comparisons with baselines become biased.

Editorial extensions

If this is right

The large G-YOLOv11 model runs at 2.4 ms per image on an NVIDIA A10 GPU, making real-time fracture screening plausible on hardware of that class.
Deploying the detector in a CAD system would require roughly one-third of the storage (15.3 MB versus 48.8 MB) and far fewer FLOPs, easing integration into clinical workstations.
The same recipe, ghost convolution plus halved filters, scales across nano, small, medium, and extra-large configurations with proportional savings in parameters and FLOPs as reported in Table 2.
The accuracy cost on this dataset is bounded: no more than an 8.4% relative mAP@0.5 difference from the corresponding YOLOv11 configuration, according to Table 3.
Compared with the large YOLOv8, YOLOv9, YOLOv10, and YOLOv11 baselines in Table 5, G-YOLOv11l has the lowest parameter count, FLOPs, and inference time among the compared models.

Reading between the lines

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

If the train/test split is not patient-disjoint, the reported mAP values likely overstate performance on new children; a patient-disjoint split would be a fairer test of whether the efficiency gains come with the accuracy cost shown.
The design decision to halve filters is entangled with the ghost-convolution replacement, so ablating each change separately, ghost only, halving only, then both, would show which one drives the efficiency gain.
The same lightweight recipe could be applied to other YOLO versions or to other small-object medical detection tasks, but the accuracy transfer would need revalidation on datasets with class imbalance similar to wrist X-rays.
For bedside deployment, the 2.4 ms timing on an A10 GPU should be re-measured on lower-power clinical hardware or with integer quantization, since deployed hardware is unlikely to match that GPU.

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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 you should know. The architecture is a straightforward, well-documented combination: replace standard convs with ghost convolutions from GhostNet and C3k2 with C3Ghost in YOLOv11, and halve filter counts. Code and pretrained weights are public. The reported resource reductions are real and reproducible. But the headline mAP numbers are not reliable, because the test set is a random image-level split of GRAZPEDWRI-DX, not patient-stratified.

What is actually new: none of the components are new; the novelty is the specific configuration for pediatric wrist fracture detection. That's fine for an application paper, but the 'state-of-the-art efficiency benchmark' claim is overreach.

What it does well: the paper includes detailed per-module filter counts, loss curves, PR curves, and qualitative examples that show false positives. It reproduces comparison detectors under the same split. The writing is transparent about the accuracy drop (13.6% mAP@0.5 vs YOLOv11l).

Soft spots: the split issue is load-bearing. GRAZPEDWRI-DX has 20,327 images from 6,091 patients; a random 70/20/10 split puts many patients' images in both train and test. Since serial wrist images are near-duplicates, the model can memorize patient-level features, inflating mAP and possibly biasing relative comparisons. The paper follows [10]'s split, but that doesn't address the problem. Second, the efficiency gain is confounded: ghost convolution and filter halving change together, so you can't attribute the savings to ghost convs without an ablation. Third, SOTA efficiency is asserted after comparing only to large detectors; no lightweight baselines. Also no error bars or significance tests; a couple of speed numbers differ between tables, which suggests measurement variability.

Bottom line: this is an honest engineering note with a fixable methodological flaw. The fix (patient-stratified split) is easy given the public dataset and code. I'd send it to review, mainly so the authors are asked to do that rerun and tone down the SOTA claim. The paper would then be a useful data point for practitioners, not a breakthrough.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes G-YOLOv11, a lightweight variant of YOLOv11 in which standard convolutions and C3k2 modules are replaced by GhostConv and C3Ghost modules, and the number of filters is halved. The authors report experiments on the GRAZPEDWRI-DX pediatric wrist X-ray dataset, claiming an mAP@0.5 of 0.535 for G-YOLOv11l at 2.4 ms inference time on an NVIDIA A10 GPU, a 68.7% size reduction relative to YOLOv11l, and describe G-YOLOv11l as a new state-of-the-art benchmark in efficiency. The paper includes parameter/FLOP comparisons, accuracy tables, qualitative detection examples, and a comparison with prior YOLOv8/YOLOv9/YOLOv10/YOLOv11 baselines reproduced under the same data split.

Significance. If the reported measurements are reliable, the paper demonstrates that a heavily compressed YOLOv11 retains moderate detection accuracy on a pediatric wrist radiograph dataset while offering substantial reductions in parameters, FLOPs, model size, and inference time. The availability of code and a public dataset is a strength, and the reproduction of baselines under the same split is good practice. However, the central efficiency-accuracy trade-off is not characterized statistically, the architectural comparison is confounded, and the test split may mix images from the same patient, so the benchmark claim should be treated as conditional until these points are addressed.

major comments (4)

[Section 2.1] The random 70/20/10 split is performed at the image level, while GRAZPEDWRI-DX contains 20,327 images from 6,091 patients. With roughly 3.3 images per patient, serial radiographs of the same wrist can appear in both the training and test sets. Because such near-duplicate images are highly similar in anatomy and appearance, the model can memorize patient-specific features rather than learn generalizable fracture cues, which would inflate the reported mAP@0.5 of 0.535 and bias the comparisons in Tables 3 and 5. Please re-split the data at the patient level (all images of a patient in one fold) and report the metrics on that split; this is a necessary condition for the claimed benchmark.
[Section 2.2.5 and Table 1] The efficiency gain is confounded: the proposed model changes the convolution type (Conv to GhostConv) and simultaneously halves the number of filters (e.g., YOLOv11l filters [64,128,256,512,512] become [32,64,128,256,512]). As a result, the measured reductions in parameters, FLOPs, and inference time cannot be attributed to ghost convolution specifically. To support the architectural claim, the paper needs an ablation that (a) applies ghost convolution while keeping the filter counts unchanged, and (b) applies standard convolution with halved filters. Without such an ablation, the efficiency numbers are real but their cause is undetermined.
[Abstract, Table 3, and Table 5] The inference-time claim is internally inconsistent. The abstract and Table 5 report 2.4 ms for G-YOLOv11l, while Table 3 lists a speed of 3 ms for the same model; similarly, YOLOv11l is listed as 6 ms in Table 3 but 5.5 ms in Table 5. Since the central contribution is an efficiency benchmark, this discrepancy must be resolved, and the measurement protocol (including whether preprocessing and post-processing are included) should be stated explicitly.
[Section 4.3 and Table 5] The claim of a 'new state-of-the-art benchmark in terms of efficiency' is not supported by the metrics as presented. G-YOLOv11l has the lowest detection accuracy of all compared detectors (mAP@0.5 of 0.535 vs. 0.568–0.672 for the others), and no efficiency-accuracy trade-off metric (e.g., mAP per FLOP or a Pareto-frontier analysis) is provided to justify calling this a benchmark. Moreover, all numbers come from a single training run; no error bars or repeated-seed statistics are reported, so statements such as 'reductions of 13.6%' have no demonstrated statistical significance.

minor comments (5)

[Section 2.2] The citation [20] is given to Khanam and Hussain's YOLOv11 overview, but the text credits Redmon et al. with introducing YOLO in 2015; please correct the citation to the original YOLO paper.
[Table 1] The table lists four filter values for C3k2/C3Ghost modules that are described as eight modules (e.g., YOLOv11l and G-YOLOv11l rows); please clarify how the values map to the module list or provide the repeated list explicitly.
[Abstract] The phrase 'reductions of 13.6% in mAP@0.5' is ambiguous because a lower mAP is a degradation rather than a reduction in resource use; consider rewording to 'a 13.6% relative decrease in mAP@0.5'.
[Figure 1] Figure 1 is not referenced in the text, and it is unclear whether the plotted points come from Tables 2–5 or from separate runs; please add a cross-reference and describe the data source in the caption.
[Section 2.3] Key training hyperparameters (initial learning rate, momentum, weight decay, learning-rate schedule, mosaic/mixup settings, and image size) are omitted; please state them or point to the configuration file in the repository to make the experiments reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all claimed results are empirical measurements on a public dataset with independently reproduced baselines.

full rationale

The paper makes no derivational claim in which an output quantity is defined in terms of the quantity it is supposed to predict. The central results—parameter counts, FLOPs, model sizes, inference times, and mAP values in Tables 2, 3, and 5—are measurements of trained detectors on the GRAZPEDWRI-DX test set. The size and FLOP reductions follow from the deliberately halved filter counts and ghost-convolution modules, but these are reported as design properties (Table 1) rather than as predictions, and the accuracy trade-off (13.6% lower mAP@0.5 for G-YOLOv11l) is an empirical outcome. The only self-citations, [15] and [16], support the generic statement that model-compression methods exist; they are not used to justify ghost convolutions, the specific architecture, or the benchmark claim. The ghost-convolution idea is attributed to external prior work [17]. No fitted parameter is renamed as a prediction, no uniqueness theorem is invoked, and no previously published result is relabeled as a discovery. The image-level rather than patient-level split is a potential data-leakage threat to the validity of the accuracy numbers, but that is an experimental-design concern, not circularity.

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

The paper does not introduce new mathematical entities. Its central claim rests on an architectural configuration (ghost convolution plus halved filters) and on a data-split protocol, both of which are choices carried from prior work or made ad hoc. The absence of an ablation against halved-width standard YOLOv11 means the claimed contribution of ghost convolution is not separately evidenced.

free parameters (2)

Filter halving factor = 0.5 (all Conv/C3k2 channels halved)
Chosen by hand to reduce parameters and FLOPs. The paper provides no ablation varying this factor or comparing with standard convolutions at the same width, so the efficiency gain is confounded.
Ghost convolution expansion kernel = 5x5 depthwise kernel (from GhostNet)
Adopted from the cited GhostNet paper [17] and not tuned for the X-ray domain. Treated as a fixed architectural choice, not as a fitted parameter.

assumptions (3)

domain assumption A random 70/20/10 image-level split of GRAZPEDWRI-DX is a valid evaluation protocol for pediatric fracture detection.
Section 2.1 randomly divides images without grouping patients. GRAZPEDWRI-DX has 20,327 images from 6,091 patients, so same-patient images can cross the split. If they do, reported mAP values are inflated.
domain assumption MS COCO pretrained weights transfer effectively to pediatric wrist X-ray images.
Section 2.3 initializes all detectors with COCO pretrained weights. The paper provides no evidence that natural-image features transfer to X-rays, though this is standard practice in the cited prior work.
domain assumption Ghost convolution preserves representational capacity comparable to standard convolution at the same channel width.
Assumed throughout Section 2.2.2.1. The paper does not ablate ghost versus standard convolution at matched width, so this assumption carries the efficiency-at-equal-accuracy claim.

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Cite this review

Pith. "Pith review of Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays." pith.science (2026). https://pith.science/paper/6MEWRDL2

@misc{pith2026250100647,
  author       = {Pith},
  title        = {Pith review of: Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6MEWRDL2}},
  note         = {Machine review of arXiv:2501.00647}
}

read the original abstract

Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightweight CAD system based on the YOLO detector for fracture detection. This system, named ghost convolution-based YOLOv11 (G-YOLOv11), builds on the latest version of the YOLO detector family and incorporates the ghost convolution operation for feature extraction. The ghost convolution operation generates the same number of feature maps as traditional convolution but requires fewer linear operations, thereby reducing the detector's computational resource requirements. We evaluated the performance of the proposed G-YOLOv11 detector on the GRAZPEDWRI-DX dataset, achieving an mAP@0.5 of 0.535 with an inference time of 2.4 ms on an NVIDIA A10 GPU. Compared to the standard YOLOv11l, G-YOLOv11l achieved reductions of 13.6% in mAP@0.5 and 68.7% in size. These results establish a new state-of-the-art benchmark in terms of efficiency, outperforming existing detectors. Code and models are available at https://github.com/AbdesselamFerdi/G-YOLOv11.

Figures

Figures reproduced from arXiv: 2501.00647 by the authors.

**Figure 2.** Schematic diagram of the proposed CAD system based on the G-YOLOv11 detector for localizing pediatric [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Visualization of training labels in the GRAZPEDWRI-DX training set. First row: distribution of classes and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Bounding box-annotated X-ray images of pediatric wrist trauma from the GRAZPEDWRI-DX dataset [ [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Architecture of the proposed G-YOLOv11 detector. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Plots of the training and validation loss for the YOLOv11 and proposed G-YOLOv11 detectors. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 7.** Figure 7: FScore as a function of confidence threshold, along with precision-recall curves, for the proposed G [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Sample of five images from the GRAZPEDWRI-DX test set, displaying ground-truth and predicted bounding [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [10]

Yolov9 for fracture detection in pediatric wrist trauma x-ray images

Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou, and Jen-Shiun Chiang. Yolov9 for fracture detection in pediatric wrist trauma x-ray images. Electronics Letters, 60(11):e13248, 2024

work page 2024

[2] [1]

Classification and segmentation of covid-19 cxr and chest ct images using deep learning algorithms

Abdesselam Ferdi. Classification and segmentation of covid-19 cxr and chest ct images using deep learning algorithms. Master’s thesis, 2020

work page 2020

[3] [2]

Colorization technique to improve dcnn-based ich ct image classification

Abdesselam Ferdi, Said Benierbah, and Youcef Ferdi. Colorization technique to improve dcnn-based ich ct image classification. In Proceedings of the 2nd National Conference on Telecommunications and Applications (CNTA), pages 372–376, 2022

work page 2022

[4] [3]

U-net-based covid-19 ct image semantic segmentation: A transfer learning approach

Abdesselam Ferdi, Said Benierbah, and Youcef Ferdi. U-net-based covid-19 ct image semantic segmentation: A transfer learning approach. In 2022 7th International Conference on Image and Signal Processing and their Applications (ISPA), pages 1–5. IEEE, 2022

work page 2022

[5] [4]

Residual encoder-decoder based architecture for medical image denoising

Abdesselam Ferdi, Said Benierbah, and Amir Nakib. Residual encoder-decoder based architecture for medical image denoising. Multimedia Tools and Applications, pages 1–18, 2024

work page 2024

[6] [5]

Parallelnet: Multiple backbone network for detection tasks on thigh bone fracture

Mengxuan Wang, Jinkun Yao, Guoshan Zhang, Bin Guan, Xinbo Wang, and Yueming Zhang. Parallelnet: Multiple backbone network for detection tasks on thigh bone fracture. Multimedia Systems, pages 1–10, 2021

work page 2021

[7] [6]

Deep learning-based localization and segmentation of wrist fractures on x-ray radiographs

Deepa Joshi, Thipendra P Singh, and Anil Kumar Joshi. Deep learning-based localization and segmentation of wrist fractures on x-ray radiographs. Neural Computing and Applications, 34(21):19061–19077, 2022

work page 2022

[8] [7]

Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models

Ammar Ahmed, Ali Shariq Imran, Abdul Manaf, Zenun Kastrati, and Sher Muhammad Daudpota. Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models. Biomedical Signal Processing and Control, 93:106144, 2024

work page 2024