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.

A novel algorithm based on multi-dimensional fusion is proposed for classifying lung nodules.Based on the algorithm for reducing false positives of pulmonary nodules,the optimization is carried out by introducing a high-level feature enhancement soft activation mapping module after obtaining features by the multi-scale feature fusion module to improve the classification ability.To address the imbalance of different nodule data in the actual classification,a balanced mean square error loss is adopted to improve the training effect of the model.A three-dimensional and two-dimensional model fusion method is used to further improve the classification performance.The experiment conducted on a Private Lung dataset proves that the proposed model has a classification accuracy of 93.8％,outperforming the existing methods.