School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
Sci Rep. 2023 Apr 4;13(1):5517. doi: 10.1038/s41598-023-32312-1.
The detection of pulmonary nodules has a low accuracy due to the various shapes and sizes of pulmonary nodules. In this paper, a multi-scale detection network for pulmonary nodules based on the attention mechanism is proposed to accurately predict pulmonary nodules. During data processing, the pseudo-color processing strategy is designed to enhance the gray image and introduce more contextual semantic information. In the feature extraction network section, this paper designs a basic module of ResSCBlock integrating attention mechanism for feature extraction. At the same time, the feature pyramid structure is used for feature fusion in the network, and the problem of the detection of small-size nodules which are easily lost is solved by multi-scale prediction method. The proposed method is tested on the LUNA16 data set, with an 83% mAP value. Compared with other detection networks, the proposed method achieves an improvement in detecting pulmonary nodules.
由于肺结节的各种形状和大小，肺结节的检测准确率较低。本文提出了一种基于注意力机制的多尺度肺结节检测网络，以准确预测肺结节。在数据处理过程中，设计了伪彩色处理策略来增强灰度图像并引入更多上下文语义信息。在特征提取网络部分，本文设计了一个集成注意力机制的 ResSCBlock 基本模块用于特征提取。同时，在网络中使用特征金字塔结构进行特征融合，通过多尺度预测方法解决了小尺寸结节易丢失的检测问题。所提出的方法在 LUNA16 数据集上进行了测试，其 mAP 值为 83%。与其他检测网络相比，所提出的方法在检测肺结节方面取得了改进。
