Abstract

Info

Author(s):: ZHANG Caidi1; LI Yueyang2; CUI Fangzheng2; LUO Haichi3; GU Zhongxuan2; 1. Department of Respiration, Affiliated Hospital of Jiangnan University, Wuxi 214122, China 2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China 3. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

Keywords:: Keywords: nodule candidate detection computer-aided detection strengthen coordinate attention module sphere based loss function

Abstract:: Abstract: A nodule candidate detection algorithm based on 3DSCANet utilizing deep learning techniques is proposed to improve nodule candidate detection performance. The algorithm employs a strengthen coordinate attention (SCA) module which improves upon the basic coordinate attention mechanism to enable it to extract three-dimensional (3D) features, and incorporates adaptive convolution to extract cross-channel features, thereby enhancing the feature extraction capability of the SCA mechanism. Additionally, a method to convert 3D rectangular anchor boxes into 3D spheres is proposed, along with the introduction of a sphere based intersection over union loss function (SIoUX) to fully leverage the morphological characteristics of lung nodules which are spherical in shape. During the experimental phase, the method is tested on the LUNA16 dataset using ten-fold cross-validation, and it achieves an average recall rate of 0.94.