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Effect of input size on the classification of lung nodules using convolutional neural networks

机译：输入规模对卷积神经网络肺结节分类的影响

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Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. The number of slices in a CT scan can be up to 600. Therefore, computer-aided-detection (CAD) systems are very important for faster and more accurate assessment of the data. In this study, we proposed a framework that analyzes CT lung screenings using convolutional neural networks (CNNs) to reduce false positives. We trained our model with different volume sizes and showed that volume size plays a critical role in the performance of the system. We also used different fusions in order to show their power and effect on the overall accuracy. 3D CNNs were preferred over 2D CNNs because 2D convolutional operations applied to 3D data could result in information loss. The proposed framework has been tested on the dataset provided by the LUNA16 Challenge and resulted in a sensitivity of 0.831 at 1 false positive per scan.
机译：最近的研究表明，与传统胸部射线照相相比，使用年低剂量计算断层扫描（CT）的肺癌筛选将肺癌死亡率降低20℃。因此，CT肺筛查已开始广泛使用全世界。然而，分析这些图像是放射科医师的严重负担。 CT扫描中的切片数最长可达600.因此，计算机辅助检测（CAD）系统对于更快更准确地评估数据非常重要。在本研究中，我们提出了一种使用卷积神经网络（CNNS）分析CT肺部筛查以减少误报的框架。我们用不同的体积尺寸训练了我们的模型，并显示了体积大小在系统的性能中起着关键作用。我们还使用不同的融合来展示他们的力量和对整体准确性的影响。 3D CNN优先于2D CNNS，因为应用于3D数据的2D卷积操作可能导致信息丢失。所提出的框架已经在LunA16挑战提供的数据集上进行了测试，并导致每次扫描的1个假阳性0.831的灵敏度。

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