A lightweight neural network for lung nodule detection based on improved ghost module.

Background: Computer tomography images are the preferred method of preoperative evaluation for lung disease. However, it remains difficult to detect and recognize nodules accurately and efficiently due to poor data imaging quality, heavy reliance on physician experience and the need for more human-computer interaction for diagnosis. Currently, image nodule detection based on deep convolutional neural networks has gained much momentum. Methods: To alleviate doctors' tremendous labor in the diagnosis procedure, and improve the accuracy of intelligent detection of lung nodules, we improved GhostNet and proposed a lightweight neural network for object detection for lung nodule image detection. Firstly, the bneck structure in the backbone feature extraction network is adopted and improved from the structure of MobileNetV3. The weights are adjusted by changing the initial channel attention mechanism and introducing a spatial-temporal attention mechanism. Then, in the enhanced feature extraction part, we mainly use depth-separable convolution blocks to replace the 3×3 convolution of the original network for the purpose of reducing the model parameters, and make more improvements based on the network structure to enhance the applicability of the network. Diagnostic precision, recall, F1-score, mAP and parameter count were calculated. Results: According to our lightweight neural network, F1-score, precision, and recall were 0.87, 86.34%, and 86.69%, respectively. Based on our dataset, the Yolov4-GNet network proposed in this research outperforms the current neural networks on both precision and recall as well as F1. Conclusions: The lung nodule detection method proposed in this research not only simplifies the processing of images, but also outperforms comparable methods in nodule detection rate and positioning accuracy, providing a new way for lung nodule detection.