Multiscale self-calibrated pulmonary nodule detection network fusing dual attention mechanism

Abstract

Objective. In this study, we propose a model called DEPMSCNet (a multiscale self-calibration network) that has a high sensitivity and low false positive rate for detecting pulmonary nodules. Approach. First, at the feature extraction stage, we propose to use the REPSA-MSC module instead of the traditional convolutional neural network. The module extracts multiscale information from the feature map based on the image pyramid strategy while introducing adaptive convolutional branches to detect contextual information at each position of the multiscale, thereby expanding the receptive field and improving sensitivity. At the same time, multiple branches are adaptively weighted by channel attention, and the weights of different branches are adjusted to better generate pixel-level attention. Secondly, the proposed DSAM (dual-path spatial attention module) operates at the information fusion stage. This module fully exploits the rich spatial information of CT scans, obtains receptive field information from two branches, combines low-level feature map information with high-level semantic information, and enhances location-related information to effectively improve specificity. Thirdly, the focal loss function is used to solve the problem of positive and negative sample imbalance. Main results. The proposed model has been evaluated on the public lung nodule analysis (LUNA16) challenge dataset. The technique outperforms the most recent state-of-the-art detection algorithms in terms of sensitivity and specificity, obtaining a sensitivity of 0.988 and a competitive performance metric (CPM) of 0.963. Significance. Ablation experiments show that the two modules proposed in this paper effectively reduce false positives and improve sensitivity. This model effectively reduces the number of false positive nodules that doctors see on CT scans.