Chromosome Karyotyping System Based On Convolutional Neural Network

In clinical practice,human chromosome karyotyping is critical for diagnosing genetic diseases.In the karyotyping process,the metaphase chromosomes are identified,paired,and arranged in decreasing order by size,so as to obtain the karyotype for the doctor to diagnose the genetic diseases.Traditional chromosome karyotyping is manually processed,requiring operators to have rich domain knowledge.The process is time-consuming and laborious,and the analysis efficiency is low.In order to solve the above problems,this thesis designs a series of algorithm based on the metaphase cell images.The methods of digital image processing,deep learning classification and computer software development are alpplied to design a complete chromosome karyotyping system.The designed system can realize the automation and intelligence of chromosome karyotyping,and improve the efficiency of chromosome karyotyping.The main contents of this thesis are as follows:First of all,based on the image binarization method,ostu method and contour boundary tracking method,the algorithm of eliminate impurities and segmentation extraction are designed to extract the chromosomes in the metaphase cell images.As for the overlap chromosomes and adhesion chromosomes,this thesis also designs the corresponding automatic separation and artificial auxiliary separation methods.Secondly,the gradient-based architecture search method is used to search the optimal CNN architecture on the chromosome data set.The automatically designed CNN is used to classify the extracted chromosomes.Furthermore,a correcting algorithm based on the prior knowledge in the karyotyping domain is proposed to correct the predicted label of the chromosome.The effectiveness of the automatically designed CNN for chromosome clas-sification is verified through the experiments results on a public chromosome dataset.Compared with the state-of-the-art methods,the automatically designed CNN achieves the best performances in terms of both average accuracy and overall accuracy.Besides,the experimental results also show that the proposed correcting algo-rithm can further improve the classification accuracy.Finally,this thesis integrates the above methods to development the chromosome karyotyping system.The Python is used to realize the back-end codes of system,and the Qt Designer is applied to design the operating interfaceof system.Besides,this thesis also uses the Py Installer library in Python to package the software system into an executable program in the exe format.The packaged software system no longer depends on the Python programming environment,it can be run directly on a computer without Python installed.