Lung Pattern Classification Via DCNN
Lung Pattern Classification Via DCNN
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DOI：
10.1109/bigdata50022.2020.9378090
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
J. He;Meng Han;Lei Yu;Chao Mei
中科院分区：
文献类型：
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作者：
J. He;Meng Han;Lei Yu;Chao Mei
间质性肺疾病（ILD）会导致肺纤维化。 ILD的正确分类在诊断和治疗过程中起着至关重要的作用。在这项研究工作中，我们提出了一种基于深度卷积神经网络（DCNN）和全局特征的肺结节识别方法，可用于肺结节全局特征的计算机辅助诊断（CAD）。首先，根据肺部计算机断层扫描（CT）图像的特点和复杂性构建了DCNN。然后讨论了不同迭代次数对识别结果的影响以及不同模型结构对肺结节全局特征的影响。我们还结合了卷积核大小、特征维度和网络深度的改进。第三，分析了我们提出的不同池化方法、激活函数和训练算法的效果，以证明新策略的优势。最后，实验结果验证了所提出的 DCNN 用于肺结节全局特征 CAD 的可行性，并且评估表明，与现有技术相比，我们提出的方法可以取得出色的结果。
Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we propose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then we discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also incorporated the improvement of convolution kernel size, feature dimension, and network depth. Thirdly, the effects of different pooling methods, activation functions and training algorithms we proposed has been analyzed to demonstrate the advantages of the new strategy. Finally, the experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules, and the evaluation shown that our proposed method could achieve an outstanding results compare to state-of-arts.