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Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN)

机译:利用CNN特征(R-CNN)的卷积神经网络(CNN)和区域检测和分类肺异常(R-CNN)

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Image-based computer-aided diagnosis (CADx) algorithm by use of convolutional neural network (CNN) does not necessarily require an image-feature extractor. Therefore, image-based CADx is powerful compared with feature-based CADx that requires the image-feature extractor for differential diagnosis of lung abnormalities such as lung nodules and diffuse lung diseases. We have also developed an image-based computer-aided detection (CADe) algorithm by use of regions with CNN features (R-CNN) for detection of lung abnormalities. We evaluated the performance of image-based CADx by use of CNN and that of image-based CADe by use of R-CNN for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.
机译:通过使用卷积神经网络(CNN)的基于图像的计算机辅助诊断(CADX)算法不一定需要图像特征提取器。因此,与基于特征的CADX相比,基于图像的CADX是强大的,其需要图像特征提取器用于鉴别诊断肺结节和弥漫性肺病等肺异常。我们还通过使用具有CNN特征(R-CNN)的区域来开发基于图像的计算机辅助检测(CADE)算法,用于检测肺异常。我们通过使用CNN和使用R-CNN来评估基于图像的CADX的性能,并通过使用R-CNN用于各种肺部异常,例如肺结节和弥漫性肺病。

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