A pulmonary nodule image deep learning identification system based on an RGB channel overlay method and a method thereof

Abstract

The invention provides a pulmonary nodule image deep learning identification system based on an RGB channel overlay method and a method thereof. The system comprises a data reading module, a pulmonary parenchyma extracting module, a pulmonary nodule extracting module, an RGB channel overlay module, a sample training module and a prediction module. According to the invention, pre-processing operation, such as RGB channel overlay, is performed on original data, and a great amount of training set data are trained by using a deep learning method, and then accurate identification of images is achieved via a prediction model obtained after training.

Description

technical field [0001] The present invention relates to a technology in the field of image processing, in particular to a lung nodule image deep learning recognition system and method based on an RGB channel superposition method. Background technique [0002] Pulmonary nodules are a multi-system and multi-organ granulomatous disease of unknown etiology. As a patient can have as many as hundreds of lung CT slices, and some pulmonary nodules may be small in size, it is time-consuming and laborious to judge with the naked eye . Therefore, computer-aided diagnosis can play a very good role in helping. Contents of the invention [0003] The present invention aims at the fact that the existing technology cannot extract effective features through deep learning and cannot combine multiple continuous CT slices for analysis, so as to analyze the longitudinal direction of the lung structure, but can only perform convolutional neural network through a single lung CT slice. In view o...

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Problems solved by technology

[0003] The present invention aims at the fact that the existing technology cannot extract effective features through deep learning and cannot combine multiple continuous CT slices for analysis, so as to analyze the longitudinal direction of the lung structure, but can only perform convolutional neural network through a single lung CT slice. In view of the shortcomings of learning and training, a deep learning recognition system and method for pulmonary nodule images based on the RGB channel superposition method are proposed. By performing preprocessing operations such as RGB channel superposition on the original data, a large number of training set data are processed by deep learning methods. Training, and accurate recognition of images through the trained prediction model

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