Classification of Chromosome Karyotype Based on Faster-RCNN with the Segmatation and Enhancement Preprocessing Model

Prenatal screening of chromosomal abnormalities is an important means of ensuring the healthy survival rate of newborns. The complex information and tedious workload of chromosome karyotype image analysis is a major difficulty in medical diagnosis. In this paper, a preprocessing model with object segmentation and feature enhancement is proposed. Combined with the framework of deep learning network, an automatic classification model for karyotype recognition of chromosomes is constructed. The preprocessing model studies the extraction of chromosome karyotype images at the pixel level and the feature enhancement of chromosome karyotype images. The model aims at providing more interpretable information for the deep learning network. In this paper, the algorithm analysis of chromosome karyotype preprocessing is carried out, the classification recognition network is built, and the detection results of the network verify the positive role of the preprocessing model. The model of chromosome karyotype automatic analysis based upon deep learning network may provide accurate reference information for doctors and reduce the workload of repeated diagnoses, which has very high application values.