Gastrointestinal tumor microscopic hyper-spectral image processing method based on convolutional neural network

Abstract

The invention discloses a gastrointestinal tumor microscopic hyper-spectral image processing method based on a convolutional neural network, comprising the following steps: reducing and de-noising the spectral dimension of an acquired gastrointestinal tissue hyper-spectral training image; constructing a convolutional neural network structure; and inputting obtained hyper-spectral data principal components (namely, a plurality of 2D gray images, which are equivalent to a plurality of feature maps of an input layer) as input images into the constructed convolutional neural network structure using a batch processing method, and by taking a cross entropy function as a loss function and using an error back propagation algorithm, training the parameters in the convolutional neural network and the parameters of a logistic regression layer according to the average loss function in a training batch until the network converges. According to the invention, the dimension of a hyper-spectral image is reduced using a principal component analysis method, enough spectral information and spatial texture information are retained, the complexity of the algorithm is reduced greatly, and the efficiency of the algorithm is improved.

Description

technical field [0001] The invention relates to the field of medical hyperspectral image processing, in particular to a convolutional neural network-based microscopic hyperspectral image processing method for gastrointestinal tumors. Background technique [0002] Tumor refers to the new organism formed by the proliferation of local tissue cells under the action of various tumorigenic factors, because this new organism is mostly in the form of space-occupying blocky protrusions, also known as neoplasm. Gastrointestinal tumors are common tumors in the digestive tract. Gastrointestinal benign tumors are less likely to recur after resection and are less harmful to the body; gastrointestinal malignant tumors, namely cancer, are the most common malignant tumors with a high incidence worldwide. One, and the incidence rate has increased significantly in recent years. [0003] At present, when performing pathological examination on gastrointestinal tumors, it is necessary to perform...

AI Technical Summary

Problems solved by technology

A large number of medical images require doctors to make judgments through visual observation. On the one hand, it causes a heavy workload for doctors; on the other hand, visual observation is highly subjective and difficult to conduct quantitative analysis.

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