Evidence map›Paper›PMID 40951344›Full record

ArticleFrontiers in oncology2025

Application and improvement of YOLO11 for brain tumor detection in medical images.

Weijuan Han, Xinjie Dong, Guixia Wang, Yuwen Ding, Aolin Yang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Weijuan HanSchool of Mechanical and Electronic Engineering, Zhongyuan Institute of Science and Technology, Zhengzhou, China.
Xinjie DongInformation and Communication Department, Henan Public Security Department, Zhengzhou, China.
Guixia WangSchool of Mechanical and Electronic Engineering, Zhongyuan Institute of Science and Technology, Zhengzhou, China.
Yuwen DingEditorial Department, Henan Medical College, Zhengzhou, China.
Aolin YangSchool of Public Administration, Henan University of Economics and Law, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain tumors pose a critical threat to human health, and early detection is essential for improving patient outcomes. This study presents two key enhancements to the YOLOv11 architecture aimed at improving brain tumor detection from MRI images. First, we integrated a set of novel attention modules (Shuffle3D and Dual-channel attention) into the network to enhance its feature extraction capability. Second, we modified the loss function by combining the Complete Intersection over Union (CIoU) with a Hook function (HKCIoU). Experiments conducted on a public Kaggle dataset demonstrated that our improved model reduced parameters and computations by 2.7% and 7.8%, respectively, while achieving mAP50 and mAP50-95 improvements of 1.0% and 1.4%, respectively, over the baseline. Comparative analysis with existing models validated the robustness and accuracy of our approach.

Indexed as

attentionbrain tumorgiga floating point operations per second (GFLOPs)intersection over union (IoU)mean average precision (MAP)object detectionyou only look once (YOLO)

Identifiers

PMID40951344
PMCPMC12425724

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.