Evidence map›Paper›PMID 41781664›Full record

ArticleScientific reports2026

Yolov8n based on dynamic serpentine convolution and multi-feature attention for MRI brain cranial tumor segmentation.

Yiliu Hang, Qiong Zhang, Li Li, Chunhua Lin

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yiliu HangSchool of Information Engineering, Nantong Institute of Technology, Nantong, China.
Qiong ZhangSchool of Information Engineering, Nantong Institute of Technology, Nantong, China. 18862928127@163.com.
Li LiSchool of Information Engineering, Nantong Institute of Technology, Nantong, China.
Chunhua LinSchool of Information Engineering, Nantong Institute of Technology, Nantong, China.

Funding

the program of the General Program of Nantong Science and Technology Bureau MSZ2024110the Program of the Nantong Institute of Technology Doctoral Research Start-up Fund 2025XKB30
6 · The paper itself

Abstract

For MRI brain tumor image segmentation, it is necessary to have high real-time and accurate edge localization. Therefore, we propose a yolov8n based on dynamic serpentine convolution and multi-feature attention method (DMA-YOLOV8n). The method combines dynamic serpentine convolution and multi-feature attention mechanism, which can effectively adapt to different brain tumor tissue edge morphology changes and more accurately segmented to obtain brain tumor and locate its edge position. First, dynamic serpentine convolution is used to replace standard convolution in C2f. module. Then, drawing on the idea of skip connection in U-Net model, multi- feature fusion is used to connect multilayer sampling information to retain more feature details and improve edge segmentation accuracy. Finally, dual attention mechanism is added to multilayer feature fusion to pay more attention to brain tumor tissue. DMA-YOLOV8n is applied to brain MRI images from Kaggle_3M dataset. Experimental results show the method has mAP50: 0.806 and mAP50:95: 0.490.

Indexed as

Brain NeoplasmsImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsConvolutional Neural NetworksHumansBrain tumor segmentationDual-attention mechanismDynamic serpentine convolutionMulti-feature fusionYOLOV8n

Identifiers

PMID41781664
PMCPMC13068969

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