通过聚本地和语特征融合,以注意引导的深度框架来定位和随后通过聚本地和语特征融合进行分类
Pradipta Sasmal1, Susant Kumar Panigrahi2, Swarna Laxmi Panda3
1Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India. pradipta@ee.iitkgp.ac.in.
Medical & biological engineering & computing
|May 2, 2025
概括
这项研究引入了一种使用深度学习 (YOLOv4) 的自动化方法,用于从视频中检测,定位和分类结肠多. 该技术实现了高精度,有助于早期结直肠癌诊断.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 结肠直肠癌 (CRC) 是一个主要的全球健康问题.
- 准确的多体检测和分类对于早期诊断和治疗至关重要.
- 现有的方法可能缺乏实时结肠镜分析的精度.
研究的目的:
- 开发一个自动化的深度学习模型,用于在结肠镜视频中检测,定位和分类.
- 在YOLOv4架构中使用空间和道注意力机制来增强多位本地化.
- 通过融合深层和手工制作的特征,将多细胞分为腺瘤或非腺瘤.
主要方法:
- 利用YOLOv4深度学习模型与空间和通道注意力块用于多位本地化.
- 用于局部形状特征提取的定向梯度 (PHOG) 的金字塔直方图.
- 整合了三重的语网络,以学习聚体内歧视性特征.
- 融合深度和手工制作的特征用于聚类的分类.
主要成果:
- 实现了高聚合物定位性能,平均精度为0.8971 (Kvasir-SEG) 和0.9171 (SUN).
- 证明了聚类型 (腺瘤/非腺瘤) 的96.66%的可靠分类准确度.
- 在分类框架中获得96.54%的F1评分,验证了其有效性.
结论:
- 拟议的自动化技术有效地检测,定位和分类结肠镜视频中的息肉.
- 聚合YOLOv4,注意力机制,PHOG和语网络提供了一个强大的诊断工具.
- 这种方法在结肠镜检查中显示出实时和离线分析的巨大潜力,有助于预防结肠直肠癌.
相关概念视频
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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