基于CT扫描图像开发用于COVID-19诊断的深度神经网络模型
Javad Hassannataj Joloudari1, Faezeh Azizi1, Issa Nodehi2
1Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
一个新的6层深度神经网络 (DNN) 模型使用CT扫描准确诊断COVID-19. 这种人工智能方法实现了96.71%的准确性,超过了以前检测病毒的方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 胸部X射线和计算机断层扫描 (CT) 扫描显示出在诊断COVID-19中的潜力.
- 当前的人工智能 (AI) CT诊断系统面临复杂神经网络的挑战,导致训练困难和高计算率.
研究的目的:
- 开发一种轻量级的6层深度神经网络 (DNN) 模型,以使用CT扫描图像改善COVID-19诊断.
- 提高对患有和没有COVID-19的个人进行分类的准确性.
主要方法:
- 开发了一个6层深度神经网络 (DNN) 模型,用于从CT图像中诊断COVID-19.
- 采用全球特征提取器操作员进行图像特征提取.
- 使用十倍交叉验证技术进行数据分区 (培训,测试,验证).
- 在没有神经元脱落的情况下训练了DNN模型.
主要成果:
- 拟议的轻量级DNN模型实现了96.71%的诊断准确性.
- 与其他研究的分类模型 (决策树,随机森林,标准神经网络) 相比,DNN模型表现出更高的性能.
结论:
- 开发的6层DNN模型提供了使用CT扫描进行COVID-19诊断的高度准确和高效的方法.
- 这种人工智能驱动的方法为传统的COVID-19测试方法提供了一个有希望的替代方案或补充.
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