Convolutional neural network-based chromosome important feature visualization method and device

Abstract

The invention provides a chromosome important feature visualization method based on a convolutional neural network. The method comprises the following steps: training a preset convolutional neural network model by using a training data set; inputting the training data set into the trained convolutional neural network model to obtain a classification result, a weight result and a feature result of each chromosome picture; multiplying the feature result and the weight result of each chromosome picture to obtain first importance information of each chromosome picture; averagely mapping the first importance information of each chromosome picture to a chromosome stripe corresponding to the chromosome picture to obtain longitudinal importance information; performing statistical analysis on each chromosome picture according to the longitudinal importance information and the classification result of each chromosome picture, obtaining and displaying the important stripe features of the convolutional neural network model for identifying various chromosomes, and realizing visual display of the chromosome important stripe features of the convolutional neural network for discriminating chromosome categories.

Description

technical field [0001] The present invention relates to the field of intelligent medical technology, in particular to a convolutional neural network-based method and device for visualizing important features of chromosomes. Background technique [0002] Human chromosome identification is an important research topic in medical genetics. It has a wide range of applications in the fields of medical clinical diagnosis, auxiliary teaching and scientific research. It is an important basis for judging human genetic diseases. With the development of artificial intelligence, convolutional neural networks are widely used and very effective in the field of image processing. The convolutional neural network has achieved good results in the identification of chromosomes, with higher accuracy and faster speed, which can effectively reduce the burden on doctors. [0003] However, in the existing technology, although the convolutional neural network has achieved high accuracy in the identi...

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Problems solved by technology

[0003] However, in the existing technology, although the convolutional neural network has achieved high accuracy in the identification of chromosomes, it cannot completely replace manual identification, and it cannot make people understand the features it extracts. Visual display of important band features of chromosomes

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