使用深度学习对支气管镜诊断图像进行呼吸系统疾病的准确预测
Weiling Sun1, Pengfei Yan2, Minglei Li2
1Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin 150040, China; Department of Endoscope, Harbin Medical University Cancer Hospital, Harbin 150040, China.
Journal of advanced research
|November 21, 2024
概括
一种新的深度学习模型,即多尺度注意力残留网络 (MARN),可以从支气管镜像中准确诊断肺部疾病. 这种人工智能工具显示出高精度,帮助医生及时诊断呼吸系统疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 支气管镜检查对于诊断和治疗呼吸系统疾病至关重要.
- 深度学习系统可以提高气管,支气管和肺部疾病的诊断准确性.
- 提高算法准确度是有效的AI辅助支气管镜诊断的关键.
研究的目的:
- 开发一个多尺度的注意力残留网络 (MARN) 用于通过支气管镜图像来诊断肺部疾病.
- 通过使用多尺度卷积块注意模块 (MCBAM) 增强用于病变识别的特征提取.
- 通过梯度加权类激活地图 (Grad-CAM) 提高诊断的解释性.
主要方法:
- 来自615例患者的2900张支气管镜图像的数据集被随机分区用于培训,验证和测试.
- 开发了MARN模型并与其他算法进行了比较.
- 模型的性能与三个医生的诊断进行了评估.
主要成果:
- 马恩模型实现了97.76%的准确性和99.79%的AUC正常与损伤图像.
- 对于良性与恶性病变,准确率为92.26%,AUC为96.82%.
- 该模型显示正常,良性和恶性病变的准确率为93.10%,AUC为99.02%.
结论:
- 与其他方法相比,MARN网络显示出优越的诊断性能.
- 该模型的准确性与经验丰富的医生可比,效率明显更高.
- 马恩显示出 bronchoscopic 图像的精确评估的巨大潜力,协助临床医生.
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