Transfer Learning by Cascaded Network to Identify and Classify Lung Nodules for Cancer Detection
Transfer Learning by Cascaded Network to Identify and Classify Lung Nodules for Cancer Detection
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DOI：
10.1007/978-981-15-4818-5_20
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发表时间：
2020-02
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影响因子：
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通讯作者：
Shah B. Shrey;Lukman Hakim;M. Kavitha;H. Kim;Takio Kurita
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作者：
Shah B. Shrey;Lukman Hakim;M. Kavitha;H. Kim;Takio Kurita
肺癌是世界上最致命的疾病之一。在早期阶段检测这种肿瘤可能是一项繁琐的任务。现有的用于肺结节识别的深度学习体系结构复杂，参数较多。本研究开发了一种级联结构，能够准确地分割和分类CT图像上的良、恶性肺结节。本文的主要贡献在于引入了一种分割网络，在该网络中，第一阶段训练在公共数据集上，可以通过转移学习的方法从任意数据集中识别包含结节的图像。而对结节的分割改进了第二阶段，将结节分为良性和恶性。该体系结构的性能优于传统方法，曲线下面积达到95.67%。实验结果表明，对于肺癌检测中的肺结节分类，97.96%的分类准确率优于其他简单和复杂的分类体系。
Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from any data set by means of transfer learning. And the segmentation of a nodule improves the second stage to classify the nodules into benign and malignant. The proposed architecture outperformed the conventional methods with an area under curve value of 95.67%. The experimental results showed that the classification accuracy of 97.96% of our proposed architecture outperformed other simple and complex architectures in classifying lung nodules for lung cancer detection.