GastroFuse-Net:一个集体深度学习框架,旨在检测内镜图像中的胃肠道异常
Sonam Aggarwal1, Isha Gupta1, Ashok Kumar2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Mathematical biosciences and engineering : MBE
|November 1, 2024
概括
这项研究介绍了GastroFuse-Net,这是一种新的深度学习模型,用于从内镜图像中诊断胃肠道疾病. 该模型实现了高精度,为医疗诊断中手动解释的挑战提供了潜在的解决方案.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 用于胃肠道疾病诊断的内镜图像的手动解释是劳动密集的,耗时的,容易变化.
- 从内镜图像中准确和自动诊断胃肠道疾病仍然是一个重大挑战,即使是经验丰富的专家.
研究的目的:
- 开发和评估一个专门的卷积神经网络 (CNN) 架构,GastroFuse-Net,用于从内镜图像自动识别人类胃肠道疾病.
- 通过利用深度学习技术,提高胃肠道疾病诊断的效率和一致性.
主要方法:
- 一个基于CNN的新型架构,GastroFuse-Net,是通过整合浅层和深层CNN模型的功能来捕捉各种图像表示而设计的.
- 克瓦西尔数据集包括由解剖结构,疾病和外科手术分类的内镜图像,用于模型培训和验证.
- 通过使用包括精度,回忆,特异性,F1得分,马修相关系数 (MCC) 和准确性在内的指标来评估性能.
主要成果:
- 在Kvasir数据集上,GastroFuse-Net表现出了卓越的表现.
- 该模型实现了0.985的精度,0.985的召回,0.985的特异性,0.984的F1得分,0.997的MCC,0.982的MCC,和98.5%的整体精度.
结论:
- 作为一种自动化工具,GastroFuse-Net显示出显著的希望,用于从内镜图像中诊断胃肠道疾病.
- 拟议的深度学习方法为手动诊断程序提供了一个高度准确和高效的替代方案,有可能减少变化并改善患者护理.
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