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Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network

机译：深度学习卷积神经网络对肺部CT图像中小肺结节良恶性分化的初步研究

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The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.

机译：对于大多数放射科医生来说，在肺部CT图像中发现的小肺结节（直径<20 mm）的良恶性鉴别诊断是一项巨大的挑战。在这里，我们通过使用深度学习卷积神经网络（CNN）介绍了肺部CT图像中小肺结节的良性和恶性分化的初步研究。从三个数据源中收集了921例经病理学证实为良性和恶性小肺结节的病例，并将其用于训练和验证CNN。提出并讨论了ROC曲线的AUC的初步结果，该结果可用于区分具有各种类型和大小的实性，半实性和磨玻璃结节的良性和恶性肺小结节。

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《SPIE Medical Imaging Conference》|2018年|1057916.1-1057916.11|共11页
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Yangfan Ni; Haozhe Huang; Dezhong Zheng; Jianyong Sun; Yuanyuan Yang; Mingqing Wang; Yiping Gu; Ming Li; Guozhen Zhang; Wentao Li; Jianguo Zhang;
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Convolutional Neural Network; pulmonary nodules; Tensorflow; medical images;

机译：卷积神经网络肺结节; Tensorflow;医学影像;
入库时间 2022-08-26 14:33:36

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1. Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT [J] . Radiology . 2020,第2期

机译：利用卷积神经网络来确定胸部CT的体积变化的态度从恶性肺结结分化
2. Mobile health framework based on adaptive feature selection of deep convolutional neural network and QoS optimisation for benign-malignant lung nodule classification [J] . Xiao Wang, Huiming Gao, Juanjuan Zhao, International journal of wireless and mobile computing . 2019,第3期

机译：基于自适应特征选择的移动健康框架，对深卷积神经网络和QoS优化对良性恶性肺结节分类
3. Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network [J] . Yi-Ming Xu, Teng Zhang, Hai Xu, Cancer Management and Research . 2020,第4期

机译：CT图像深度学习图像：使用卷积神经网络进行自动肺结核检测，用于随后的管理
4. Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network [C] . Yangfan Ni, Haozhe Huang, Dezhong Zheng, SPIE Medical Imaging Conference . 2018

机译：深受深层学习卷积神经网络促进肺CT图像小肺结节良恶性分化的初步研究
5. Deep Learning Convolutional Neural Network Applications in Image Analysis to Identify Defects [D] . Umakanth, Srijith Parakkat 2018

机译：深度学习卷积神经网络在图像分析中识别缺陷的应用
6. Computer-aided diagnosis of lung nodule classification between benign nodule primary lung cancer and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning [O] . Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, -1

机译：使用转移学习的深度卷积神经网络对不同图像大小的良性结节原发性肺癌和转移性肺癌之间的计算机辅助诊断
7. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning [O] . Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, 2018

机译：计算机辅助诊断良性结节，原发性肺癌和转移肺癌在不同图像大小与转移学习的不同图像尺寸

Preliminary study of benign and malignant differentiation of small pulmonary nodules in Lung CT images by Using Deep Learning Convolutional Neural Network

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