A New Object Detection Algorithm Based on YOLOv3 for Lung Nodules

Abstract

Lung cancer has always threatened people's health and life. Lung nodules, as early features of lung cancer, have very important clinical significance and research value for the diagnosis of lung cancer. The features captured by the traditional convolutional neural network are limited, in addition, traditional YOLO method has the problems of low accuracy and inaccurate positioning. Aiming at this problem, this paper proposes a new algorithm based on YOLOv3 for detecting lung nodules. The Inception ResBlocks are added to the feature network of YOLOv3, so that the network can extract richer feature information, furthermore, a new bounding box regression loss function is proposed. The loss function GDIoU loss makes the prediction of bounding box regression more accurate and further improves the performance of lung nodule detection. After experimental verification, the AP of this model can reach 83.5%, and the sensitivity can reach 92.6%. The proposed method has a good performance in terms of positioning accuracy and detection rate, and can avoid the problems of false detection and missed detection to a certain extent. It provides a new idea for the detection of lung nodules.