使用内镜图像分类进行胃肠道诊断的深度合体学习
Samra Siddiqui1, Junaid Ali Khan1, Shabbab Algamdi2
1Department of Computer Science, HITEC University, Taxila, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
转移学习辅助胃肠科医生诊断胃肠道 (GIT) 图像. 使用NasNet-Mobile和EfficientNet的深度组合模型在从内镜图像中分类GIT疾病时获得了高精度 (97.83-98.45%).
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道 (GIT) 异常是导致死亡的主要原因.
- 通过内镜诊断GIT障碍,存在诸如有限的注释图像和图像质量差等挑战.
- 计算机辅助诊断 (CAD) 系统对于协助胃肠科医生至关重要.
研究的目的:
- 开发一种基于转移学习的深层组合模型,用于准确分类内镜GIT图像.
- 为了提高诊断胃肠道疾病的准确性和效率.
- 为了解决现有的内镜CAD系统的局限性.
主要方法:
- 通过使用NasNet-Mobile和EfficientNet.Net的加权投票组合,制定了一个深度组合模型.
- 从内镜图像中提取了感兴趣的区域 (受影响区域).
- 使用HyperKvasir,Kvasir v1和Kvasir v2数据集进行了交叉数据集评估.
主要成果:
- 拟议的模型实现了高精度,在Kvasir v1上达到97.83%,在Kvasir v2.2.上达到98.45%.
- 性能指标包括准确性,精度,回忆力,曲线下面积 (AUC) 和F1分数.
- 该模型与现有的转移学习模型相比,表现优越.
结论:
- 转移学习是开发胃肠病学中强大的CAD系统的强大方法.
- 拟议的深层组合模型有效地从内镜图像中对GIT疾病进行分类.
- 这一框架为改善诊断准确性和患者结果提供了一个有前途的工具.
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