对基于深度学习的传染病预测和预后方法进行全面分析
Kavita Thakur1, Manjot Kaur1, Yogesh Kumar2
1Desh Bhagat University, Mandi Gobindgarh, Punjab India.
Archives of computational methods in engineering : state of the art reviews
|June 26, 2023
概括
人工智能 (AI) 模型可以从胸部X射线图像中检测和分类多种传染病. InceptionResNetV2的深度学习模型在识别COVID-19和结核病等疾病时实现了88%的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 传染病对全球健康构成重大威胁.
- 准确及时诊断对于有效的疾病管理和控制至关重要.
- 人工智能为分析医疗数据和改善诊断能力提供了强大的工具.
研究的目的:
- 使用深度学习模型检测和分类多种传染病.
- 评估各种深度学习架构在多种胸部X射线图像数据集上的性能.
- 确定最有效的深度学习模型用于传染病分类.
主要方法:
- 利用了29252张胸部X射线图像的数据集,涵盖了COVID-19,肺结核,肺炎和其他疾病.
- 采用了包括EfficientNet,NASNetLarge,DenseNet169,ResNet152V2和InceptionResNetV2.2.在内的深度学习模型
- 应用图像预处理技术,如增强和对比度增强,以及特征提取方法.
主要成果:
- 使用RGB直方图进行探索性数据分析,以可视化像素强度并识别异常.
- 在测试过程中,InceptionResNetV2模型表现出卓越的性能,达到88%的最高准确度.
- 在InceptionResNetV2模型也产生了最好的损失值 (0.399) 和根平均平方误差 (0.63).
结论:
- 深度学习模型显示出从医学图像中自动检测和分类传染病的巨大潜力.
- 在这种诊断任务中,InceptionResNetV2架构是一个非常有效的模型.
- 由人工智能驱动的工具可以提高诊断传染病的速度和准确性,帮助公共卫生工作.
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