从使用MobileNet模型的胸部X射线图像检测肺炎
Mana Saleh Al Reshan1, Kanwarpartap Singh Gill2, Vatsala Anand2
1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Healthcare (Basel, Switzerland)
|June 10, 2023
概括
一个深度学习模型有效地区分正常和严重的肺炎使用胸部X射线. 移动网络模型实现了最高的准确性,证明了其改善肺炎诊断的潜力.
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:
- 肺炎是全球主要的死亡原因,通常在视觉上与结核病等其他呼吸道疾病相似.
- 胸部X射线采集和处理的变化使精确的肺炎检测变得复杂.
- 开发强大的,数据驱动的算法来识别肺炎至关重要.
研究的目的:
- 开发和验证一种深度学习模型,使用胸部X射线图像来区分正常和严重的肺炎病例.
- 为了比较八个预训练的深度学习模型对肺炎分类的性能.
主要方法:
- 设计了一个深度学习系统,包括八个预训练模型:ResNet50,ResNet152V2,DenseNet121,DenseNet201,Xception,VGG16,EfficientNet和MobileNet.
- 这些模型在两个不同的胸部X射线数据集 (5,856和112,120张图像) 上进行了训练和评估.
- 进行超参数调整,包括批量大小,时代和优化器,用于模型优化.
主要成果:
- 移动网络模型显示了最高的分类准确性,在一个数据集上达到94.23%,在另一个数据集上达到93.75%.
- 对比分析发现,在八个测试模型中,MobileNet是最有效的模型.
- 该研究基于关键超参数系统评估模型性能.
结论:
- 深度学习模型,特别是MobileNet,显示出从胸部X射线准确检测肺炎的重大前景.
- 这些发现突出了人工智能在改善肺炎诊断方面的潜力,解决了图像变化带来的挑战.
- 建议使用各种成像技术和专家分析进行进一步验证.
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