多阶段联络增强的视网膜 DeepLabv3+ FusionNet用于增强检测和分类消化道的出血区域
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Vandalur - Kelambakkam, Chennai, 600127, Tamilnadu, India.
Scientific reports
|November 20, 2025
概括
这项研究引入了一个新的框架,用于检测和分类无线囊内镜 (WCE) 图像中的胃肠道出血. 新方法显著提高了分段和识别出血区域的准确性,有助于更早,更精确的诊断.
科学领域:
- 医疗成像医学成像
- 胃肠病学 胃肠病学
- 计算机视觉 计算机视觉
背景情况:
- 在无线囊内镜 (WCE) 中精确检测胃肠道出血对于及时诊断和治疗至关重要.
- 目前的方法难以区分出血类型和细分复杂的损伤边界.
研究的目的:
- 为改进 WCE 图像分析提出一个新的框架,即多阶段 Convo-Enhanced Retinex Canny DeepLabV3+ FusionNet.
- 为了提高出血区域细分和分类的准确性.
主要方法:
- 使用Clip-BiRetinexNet进行图像预处理,以改善对比度和纹理捕获.
- 用户使用Hough Canny-Frangi增强的DeepLabV3+来准确细分不规则的病变.
- 实现了ResNet-NaiveBayes融合,用于对出血类型进行强大的概率分类.
主要成果:
- 拟议的框架实现了高性能指标:97.6%的平均像素精度,99.2%的分类精度和99.6%的子相似系数.
- 与现有最先进的方法相比,表现出优越的性能.
结论:
- 多阶段车队增强的Retinex Canny DeepLabV3+ FusionNet在胃肠道出血的WCE图像分析方面取得了重大进展.
- 这一框架为准确的诊断和治疗规划提供了更有效的工具.
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