A pulmonary nodule image classification method for constructing feature representation based on an automatic encoder

Abstract

The invention provides a pulmonary nodule image classification method for constructing feature representation based on an automatic encoder, and relates to the technical field of computer vision. Themethod comprises the following steps: firstly, segmenting a pulmonary nodule image into local patches through superpixels; Transforming the patches into local feature vectors with a fixed length by using an unsupervised depth auto-encoder; Constructing visual vocabularies on the basis of the local features, and describing global features of the pulmonary nodule image through a visual word bag; Classifying pulmonary nodule types by using a softmax algorithm to complete the design of a model framework for representing pulmonary nodule image characteristics; Performing training by using the designed model framework and the ELCAP data set to obtain an automatic classification model for the pulmonary nodule images; And finally, carrying out pulmonary nodule image classification by using the obtained pulmonary nodule image classification model. According to the pulmonary nodule image classification method for constructing feature representation based on the automatic encoder provided by theinvention, the feature extraction capability of the pulmonary nodule classification model is improved, and the accuracy of pulmonary nodule automatic classification is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for classifying pulmonary nodule images based on feature representations constructed by an automatic encoder. Background technique [0002] Lung cancer is one of the deadliest diseases in the world, accounting for approximately 20% of all cancers in 2016. Despite recent advances in diagnosis and treatment, the 5-year cure rate is only 18.2%. Notably, if patients are diagnosed early and properly treated, their chances of survival are greatly increased. Low-dose CT scans can reduce lung cancer deaths by 20 percent, according to the National Lung Screening report. The traditional diagnosis of pulmonary nodules mainly relies on the observation of doctors and experts, so there are some disadvantages: the diagnostic results are subjective; the workload is heavy and time-consuming; human vision is limited; doctors in remote areas cannot be popularized. With the devel...

AI Technical Summary

Problems solved by technology

The traditional diagnosis of pulmonary nodules mainly relies on the observation of doctors and experts, so there are some disadvantages: the diagnostic results are subjective; the workload is heavy and time-consuming; human vision is limited; doctors in remote areas cannot be popularized

Due to the particularity of medical imaging and the constraints of privacy laws and regulations, it is impossible to obtain a large amount of training data

Images