从预处理的内镜图像中检测自身免疫性胃炎,使用深度转移学习和火焰优化.
Fadiyah M Almutairi1, Sara A Althubiti2, Shabnam Mohamed Aslam3
1Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Scientific reports
|July 10, 2025
概括
一个新的深度学习工具可以准确地从内镜图像中检测到自身免疫性胃炎 (AIG),准确率为99.33%. 这种AI进步有助于临床医生在早期AIG诊断和干预中获得更好的患者结果.
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:
- 胃道疾病 (GTD) 需要及时诊断和干预.
- 自身免疫性胃炎 (AIG) 是一种慢性GTD,早期检测至关重要.
- 内镜成像对于GTD查和严重程度评估至关重要.
研究的目的:
- 开发一种深度学习 (DL) 工具,用于从内镜图像中检测自身免疫性胃炎 (AIG).
- 在临床实践中提高AIG检测的准确性.
- 为临床医生提供人工智能辅助工具,以改善AIG诊断.
主要方法:
- 图像采集,大小调整和预处理使用透函数和火焰 (MF) 算法.
- 使用DL模型进行深度特征提取,然后进行MF算法优化和特征连接.
- 使用K-最近邻居进行分类,并通过五倍交叉验证进行性能验证.
主要成果:
- 开发的DL工具实现了AIG的99.33%的高检测精度.
- 使用单独的,连接的和融合的深度特征来评估性能.
- 该研究证明了DL模型在分析临床级内镜图像中的有效性.
结论:
- 该DL工具为从内镜数据中检测AIG提供了临床上显著的进步.
- 高精度验证了该工具在帮助临床医生诊断AIG时的潜力.
- 这种人工智能驱动的方法可以改善自身免疫性胃炎的管理.
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