Chromosome recognition method and device based on deep learning, and computer equipment

Abstract

The invention relates to a chromosome recognition method and device based on deep learning, computer equipment and a storage medium. The method comprises the following steps of acquiring a chromosomeimage to be identified; inputting a chromosome image to be recognized into the trained neural network model, and extracting chromosome morphological image features in the chromosome image to be recognized to obtain a feature map; performing multi-scale convolution processing on the feature map to obtain feature maps of different scales; classifying the feature maps of different scales to obtain the offset of chromosomes in the to-be-identified chromosome image; and performing regression processing on the offset to obtain the position of the chromosome. By adopting the method, chromosome recognition accuracy can be improved.

Description

technical field [0001] The present application relates to the technical field of chromosome identification, in particular to a method, device and computer equipment for chromosome identification based on deep learning. Background technique [0002] With the development of computer technology, artificial intelligence and deep learning have become technologies that all walks of life need to learn and combine. Chromosome identification provides valuable information for the diagnosis of human health and diseases. According to the results of chromosome identification, chromosomal diseases can be diagnosed . [0003] However, the existing chromosome identification generally uses manual microscopic examination to identify chromosomes. Image processing techniques, namely threshold segmentation and edge extraction, are used to identify chromosomes. Only part of the chromosome information will be retained, and other chromosomes that are similar to the real chromosome shape Impurities...

AI Technical Summary

Problems solved by technology

[0003] However, the existing chromosome identification generally uses manual microscopic examination to identify chromosomes. Image processing techniques, namely threshold segmentation and edge extraction, are used to identify chromosomes. Only part of the chromosome information will be retained, and other chromosomes that are similar to the real chromosome shape The impurities cannot be identified accurately, resulting in low accuracy of chromosome identification

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