A novel two-stage Lung nodule detection in medical images based on deep learning

Abstract

In order to solve the problems of low detection rate and high false positive of nodules in lung CT images by traditional computer-aided diagnosis systems. This paper introduces the real-time detection algorithm of medical images using the deep learning method. According to the three-dimensional nature of lung CT images, 3DFasterR-CNN is used to extract features to detect candidate nodules; then, the 3D convolutional neural network is used to remove false positive nodules. That saves the operation of filtering and difference map construction. The detection process mainly includes four steps: building a deep neural network, constructing suitable samples, training the network, and detecting. The results show that the algorithm has a good effect on the real-time change detection of medical images and can improve detection accuracy and efficiency. The average FROC value was 82.8%, compared with the traditional diagnosis and treatment. The recognition rate of the method has been significantly improved. The model can be used to assist doctors in diagnosing lung nodules and has a certain clinical application value.