基于深度学习的结肠疾病分类方法
Zhihe Zhao1, Zhifeng Gao1, Kun Zhang1
1Hebei University of Science and Technology.
Studies in health technology and informatics
|November 26, 2023
概括
使用A_Vit的深度学习模型在从内镜图像中分类结肠疾病时获得了95.76%的准确性. 这种人工智能工具提高了结直肠癌检测的诊断效率和准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 大肠直肠癌 (CRC) 的发病率很高,存在误诊的风险.
- 对内镜结肠图像的准确分类对于及时诊断和治疗至关重要.
- 现有的诊断方法在识别胃肠道疾病时可能缺乏效率和准确性.
研究的目的:
- 开发和评估基于深度学习的计算机辅助诊断方法,用于结肠疾病的分类.
- 提高使用内镜图像诊断胃肠道疾病的准确性和效率.
- 为临床医生提供结直肠癌检测的决策支持.
主要方法:
- 数据集预处理,包括重复删除和增强技术.
- 实施和比较两个深度学习网络架构:A_Vit和MobileNet.
- 使用Adam优化器进行模型训练,具有一致的参数和数据集.
主要成果:
- A_Vit网络架构展示了卓越的性能.
- 通过A_Vit.实现了95.76%的准确率和97.21%的召回率.
- 由于其高性能指标,A_Vit模型被选为首选选择.
结论:
- 拟议的深度学习方法显著提高了结肠疾病诊断的效率和准确性.
- 基于A_Vit的模型为胃肠病学中计算机辅助诊断提供了一个有前途的工具.
- 这种方法可以帮助临床医生做出更明智的结直肠癌管理决策.
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