Pulmonary nodule feature extraction method based on convolutional neural network and principal component analysis

Abstract

The invention discloses a pulmonary nodule feature extraction method based on a convolutional neural network and principle component analysis, which comprises the steps of firstly automatically learning pulmonary nodule features by using the convolutional neural network, performing feature extraction through convolution, and performing feature mapping through downsampling; and secondly, performing dimension reduction on the output of each feature mapping layer in a convolutional neural networks model for feature extraction (FeCNN) by using PCA (Principal Component Analysis) in feature extraction, and integrating with mapping of an output layer to acquire final multi-layer deep integration features. The pulmonary nodule feature extraction method not only can effectively recognize medical symptoms of a pulmonary nodule, but also avoids the complex feature extraction and feature reconstruction process in a traditional method, and plays a role of assisting diagnosis from an objective aspect.

Description

technical field [0001] The invention relates to a method for extracting features of pulmonary nodules based on a convolutional neural network and a principal component analysis method. Background technique [0002] The medical signs of pulmonary nodules are the basis for doctors to diagnose lung diseases. By analyzing various medical signs of lung CT images, it is convenient for doctors to judge the degree of benign and malignant nodules and make corresponding diagnostic decisions. However, doctors mainly diagnose diseases based on experience, and the diagnosis results are subject to some degree, so misdiagnosis and missed diagnosis often occur. In recent years, there is no need to manually extract features. Through the deep learning process, the data information hidden in the original input data can be extracted and abstracted layer by layer. The deeper the layer, the deeper the data concept represented by the extracted features. It cannot be expressed and obtained by the ...

AI Technical Summary

Problems solved by technology

However, doctors mainly diagnose diseases based on experience, and the diagnosis results are subjective to a certain extent, and misdiagnosis and missed diagnosis often occur.

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