<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">TACS</journal-id><journal-title-group><journal-title>Technology and Application of Computer Science</journal-title></journal-title-group><issn>2998-8926</issn><eissn>2998-8934</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2026070043</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于 KAN 增强 YOLOv10的脊柱弯曲异常智能筛查系统研究</title><url>https://artdesignp.com/journal/TACS/3/7/10.61369/TACS.2026070043</url><author>徐中一,冯立华,张颖,孟子轩,孟凡书</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>3</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-04-14</published-time></date></history><abstract>针对脊柱弯曲异常传统筛查方式主观性强、漏诊率高、智能化不足等问题，本文以 YOLOv10为基础框架，研究融合 KAN 网络的脊柱侧弯智能筛查协同设计方案。嵌入 KAN 模块可强化细微椎体非线性特征提取能力，一体化结构减少测量误差，轻量化改造实现 CPU 端落地运行。研究成果可为脊柱弯曲异常安全高效智能筛查提供技术参考。</abstract><keywords>脊柱弯曲异常, YOLOv10, KAN 网络, 关键点检测, Cobb 角测量, 轻量化模型</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]WangA,ChenH,LiuL,etal.YOLOv10:Real-TimeEnd-to-EndObjectDetection[J/OL].arXiv:2405.14458,2024.
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