A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

Abstract

Background: The rapid recognition of fetal nucleated red blood cells (fNRBCs) present considerable challenges.Objective: To establish a computer-aided diagnosis system (CAD) for rapid recognition of fNRBCs by a convolutional neural network (CNN). Methods: We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. A cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin stains. Then we proposed a CAD-based on CNN to automatically learn discriminative features and recognize fNRBCs. Region of interest 1 extraction methods were used to automatically segment individual cells in cell slices. The discriminant information from ROIs was encoded into a feature vector. The prediction network provided a pathological diagnosis. Results: Totally, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures) the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5% in the test set (1040 pictures).Conclusion: We present a CAD system for effective and accurate fNRBCs recognition based on CNN.

Comparative Study of Principle and Independent Component Analysis of CNN for Embryo Stage and Fertility Classification

“…In Thirumalaraju et al (2021) (Dirvanauskas,Maskeliunas,etal.,2019). Allpregnantladiesgoingtoforroutinarypregnancyscreeningduringsecondandthirdtrimester wererememberedfortheinvestigation.Variouspregnancy,inherentdistortionsoraneuploidieswere avoided.Gestationalagewasregisteredfromcrown-backsidelengthestimationsonfirst-trimester US19andwentfrom18to40weeks.Everyoneofthestrategiesthereforeclarifiedwereactedas per the applicable rules and regulations and supported, along with the examination.…”