Medical image lung nodule detection method based on anti-network

Abstract

The invention discloses a medical image lung nodule detection method based on an anti-network, which mainly solves the problem of low detection precision due to shading on medical image lung nodules in the prior art. The method comprises the steps of: 1) preprocessing a medical image to acquire a sample data set; 2) scaling and cutting the sample data set, and adding Gaussian noise to all processed samples to form an expanded sample data set; 3) superimposing a Faster-RCNN detector with an anti-space discarding network (ASDN) to construct a novel detector based on the anti-network; 4) trainingthe novel detector with the sample data set to obtain a trained novel detection model; and 5) performing lung nodule detection on a test data set with the trained detection model to obtain a lung nodule detection result in each medical image. The method improves the detection precision of medical image lung nodules and can be used in a computer aided diagnosis system.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for detecting pulmonary nodules in medical images, which can be used in a computer-aided diagnosis system. Background technique [0002] Pulmonary nodules are one of the most important early signs of lung cancer. According to the lesion characteristics of pulmonary nodules, the lesion characteristics of lung lesions can be inferred. Therefore, early detection and treatment of pulmonary nodules in patients with lung diseases is a key measure to reduce lung cancer mortality. Due to its high morbidity and mortality, lung cancer has become the deadliest tumor among cancers. With the change of people's living habits and the deterioration of the environment, the number of people with lung cancer is increasing, and the society is paying more and more attention to it. Combined with the medical characteristics of pulmonary nodules, using deep learning technology to proc...

AI Technical Summary

Problems solved by technology

However, because this method still uses the traditional method to recommend candidate nodules, it fails to make full use of the advantages of deep learning. Although the detection accuracy is improved, it still cannot realize the detection of pulmonary nodules in the end-to-end network.

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