Unsupervised parasite classification method and system based on artificial intelligence

Abstract

The invention discloses an unsupervised parasite classification method and system based on artificial intelligence. The classification method comprises the steps of obtaining a training data set of ato-be-detected sample; extracting feature information of the training data set by using a deep convolutional neural network VGG network; classifying the feature information by using a fuzzy C-means clustering FCM algorithm, and determining a clustering center matrix of each category; determining a clustering center vector of each category according to the clustering center matrix; determining a membership matrix according to the clustering center vector; determining an FCM loss function according to the clustering center matrix and the membership matrix; using an FCM loss function to train theVGG network, and determining the trained VGG network; and classifying cells and parasites in the training data set according to the trained VGG network and an FCM algorithm. By adopting the classification method and system provided by the invention, parasitic cells and host healthy cells can be accurately identified and classified, and the classification accuracy is improved.

Description

technical field [0001] The invention relates to the field of parasite classification, in particular to an artificial intelligence-based unsupervised parasite classification method and system. Background technique [0002] Toxoplasma gondii (Toxoplasma gondii) is a ubiquitous single-celled protozoan parasite, one-third of the world's human beings are chronically infected by Toxoplasma gondii, and most Toxoplasma gondii infections to humans are lifelong, some studies have shown that, Diseases caused by Toxoplasma gondii have become one of the largest health problems in the world; however, detecting Toxoplasma under a microscope is time-consuming and laborious. [0003] At present, there is no solution for microscopic image analysis of parasites such as Toxoplasma gondii. Due to the high similarity between parasite cells and human healthy cells, commonly used supervision algorithms require a large number of labeled parasite and cell images, while The hard clustering algorithm ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an artificial intelligence-based unsupervised parasite classification method and system to solve the problem that parasites cannot be accurately identified, and moreover, it is impossible to classify a variety of parasites or classify and distinguish them from host healthy cells

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