基于混合Inception V3与VGG16使用胸部X射线图像的混合Inception V3的COVID-19预测
K Srinivas1, R Gagana Sri1, K Pravallika2
1Department of CSE, VR Siddhartha Engineering College, Vijayawada, 520007 India.
Multimedia tools and applications
|June 26, 2023
概括
一个新的混合深度学习模型Inception V3与VGG16 (IV3-VGG) 准确地从胸部X射线中预测COVID-19. 这种自动化系统为传统诊断方法提供了一个有希望的替代方案,达到98%的准确性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 传染性疾病 传染性疾病
背景情况:
- 冠状病毒疾病-2019 (COVID-19) 诊断依赖于血清学,RT-PCR和抗原测试等方法,这些方法具有包括成本和周转时间在内的局限性.
- 胸部X射线为COVID-19查CT扫描提供了一个低成本的成像替代方案.
- 目前用于胸部X射线分析的深度学习模型面临着过度配合和错误分类等挑战.
研究的目的:
- 利用胸部X射线图像开发一个自动化的COVID-19检测系统.
- 提出一种新的混合深度学习模型,Inception V3与VGG16 (IV3-VGG),以克服现有方法的局限性.
- 评估IV3-VGG模型在预测COVID-19中的性能.
主要方法:
- 通过结合Inception V3和VGG16架构,开发了一种混合深度学习模型 (IV3-VGG).
- 该模型包含一个预处理模块来处理图像大小和强度的变化.
- IV3-VGG架构由四个块组成,第一个块为VGG16,后一个块为Inception V3,其次是分类层.
主要成果:
- IV3-VGG模型在从胸部X射线中预测COVID-19时实现了98%的高准确性.
- 混合型号的性能优于Inception V3,VGG16,ResNet50,DenseNet121和MobileNet等单个型号的性能.
- 这项研究利用了243张胸部X射线图像 (121张COVID-19阳性,122张正常) 的数据集.
结论:
- 拟议的IV3-VGG混合模型是有效的自动化COVID-19预测使用胸部X射线.
- 这种深度学习方法提供了一个高度准确和潜在的成本效益的诊断工具.
- IV3-VGG模型显示了提高COVID-19查效率和准确性的巨大潜力.
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