修改的DeeplabV3+具有多级上下文注意机制,用于结肠镜聚片细分的结肠镜
Shweta Gangrade1, Prakash Chandra Sharma1, Akhilesh Kumar Sharma1
1School of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India; School of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India.
Computers in biology and medicine
|February 6, 2024
概括
结肠镜图像的自动分析有助于早期发现癌症. 一个经过修改的DeeplabV3+模型准确地细分了息肉,提高了胃肠道疾病的诊断效率.
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:
- 结肠癌是全球主要的死亡原因,需要改进诊断方法.
- 手动分析结肠镜图像是耗时且容易出现错误的.
- 自动化计算机辅助技术对于有效和可靠的胃肠道疾病诊断至关重要.
研究的目的:
- 开发一个自动化的框架,用医学图像分析来诊断结肠镜疾病.
- 为了提高结肠镜图像中多检测和细分的准确性和效率.
- 创建一个能够自动区分多重体和其他图像特征的模型.
主要方法:
- 一个经过修改的DeeplabV3+模型被开发用于结肠镜图像中的聚细分.
- 该模型的编码器使用预训练的扩展卷积残余网络来增强特征提取.
- 修改后的模型的性能与公开数据集上的最先进的细分方法进行了评估.
主要成果:
- 经过修改的DeeplabV3+模型实现了高细分精度,CVC-Clinic DB数据集上的Dice相似系数为0.97,Kvasir-SEG数据集上的Dice相似系数为0.95.
- 拟议的模型在软件和硬件实施中都显示出更好的细分效率和有效性.
- 对DeeplabV3+架构的轻微修改导致了显著的性能提升.
结论:
- 开发的框架提供了一个强大的解决方案,用于在结肠镜检查中自动化聚细分.
- 增强的DeeplabV3+模型可以帮助内镜医生降低聚进展为癌症的风险.
- 这种计算机辅助的方法有望在诊断胃肠道疾病方面得到广泛的临床采用.
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