Computer-aided classification of lung nodules on computed tomography images via deep learning technique.
Computer-aided classification of lung nodules on computed tomography images via deep learning technique.
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DOI：
10.2147/ott.s80733
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发表时间：
2015
影响因子：
4
通讯作者：
Chen YJ
中科院分区：
文献类型：
--
作者：
Hua KL;Hsu CH;Hidayati SC;Cheng WH;Chen YJ
如果未及早诊断且存在不可切除的病变，肺癌的预后较差。由于肿瘤特征不确定，计算机断层扫描中发现的小肺结节的处理存在争议。传统的计算机辅助诊断（CAD）方案需要多个图像处理和模式识别步骤来完成定量的肿瘤分化结果。在这样的临时图像分析管道中，每一步都很大程度上取决于前一步的性能。因此，传统CAD方案中分类性能的调整是非常复杂和艰巨的。另一方面，深度学习技术具有自动利用功能和以无缝方式调整性能的内在优势。在本研究中，我们尝试利用深度学习技术简化传统 CAD 的图像分析流程。具体来说，我们在计算机断层扫描图像中的结节分类背景下引入了深度置信网络和卷积神经网络的模型。实施了两种具有特征计算步骤的基线方法进行比较。实验结果表明深度学习方法可以取得更好的判别结果，并在 CAD 应用领域有希望。
Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.