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A Modified Faster R-CNN Method to Improve the Performance of the Pulmonary Nodule Detection

机译：一种改进的快速R-CNN方法，可提高肺结节检测的性能

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In order to realize the accurate and quick positioning of pulmonary nodules in hundreds of two-dimensional CT chest images and reduce the burden of radiologist, the paper proposes a modified faster R-CNN method to improve the performance of the pulmonary nodule detection. Firstly, data enhancement technology is adopted to expand the dataset. Secondly, the image is fed into VGG-16 with de-convolution to extract the shared convolution features. Then, the shared convolution feature is sent to the region proposal network (RPN) to output candidate lung nodule region. Finally, the candidate lung nodule region and the previous shared convolution features are input into ROI pooling layer at the same time, and the characteristics of the corresponding candidate area are extracted. Through the connection layer, a multi task classifier is used to position the regression of the candidate region. According to the features of complex chest image background, large detecting object range and relatively small size of pulmonary nodule compared with natural objects, we design a smaller anchor box to accommodate changes in lung nodule size. In order to get the more accurate description of the characteristics of pulmonary nodules, we add a de-convolution layer with 4, 4, 2 and 512 for nuclear size, step size, filling size and number of nuclei respectively after the last layer of VGG-16 network conv5_3 , resulting in a higher de-convolution feature resolution. Finer granularity can be restored compared with the original feature map. The experimental results show that the average detection accuracy is up by 6.9 percentage points compared with the original model. This model can well detect solitary pulmonary nodules and pulmonary nodules and small nodules, showing certain clinical significance for early screening of lung cancer.
机译：为了实现数百个二维CT胸部图像中肺结节的准确，快速定位并减轻放射科医师的负担，提出了一种改进的快速R-CNN方法，以提高肺结节检测的性能。首先，采用数据增强技术扩展数据集。其次，将图像通过反卷积输入到VGG-16中，以提取共享的卷积特征。然后，共享卷积特征被发送到区域提议网络（RPN）以输出候选肺结节区域。最后，将候选肺结节区域和先前共享的卷积特征同时输入到ROI池化层中，并提取相应候选区域的特征。通过连接层，使用多任务分类器来定位候选区域的回归。根据胸部图像背景复杂，检测对象范围大，肺结节与自然物体相比相对较小的特点，我们设计了较小的锚盒来适应肺结节尺寸的变化。为了更准确地描述肺结节的特征，我们在VGG的最后一层之后添加了一个分别具有4、4、2和512的去卷积层，分别用于核大小，步长，填充大小和核数-16 network conv5_3，从而获得更高的反卷积特征分辨率。与原始特征图相比，可以恢复更精细的粒度。实验结果表明，与原始模型相比，平均检测精度提高了6.9个百分点。该模型可以很好地检测出孤立的肺结节，肺结节和小结节，对早期筛查肺癌具有一定的临床意义。

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