scholarly journals Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Zhidong Zhao ◽  
Yang Zhang ◽  
Zafer Comert ◽  
Yanjun Deng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrence Plot ◽  
Computer Aided Diagnosis ◽  
Fetal Hypoxia ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

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

2021 ◽  
Author(s):  
Chao Sun ◽  
Ruijie Wang ◽  
Lanbo Zhao ◽  
Lu Han ◽  
Sijia Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cord Blood ◽  
Umbilical Cord ◽  
Umbilical Cord Blood ◽  
Computer Aided Diagnosis ◽  
Blood Samples ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

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.

Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion

2019 ◽  
Vol 33 (05) ◽  
pp. 1957001
Author(s):  
Yin Dai ◽  
Daoyun Qiu ◽  
Yang Wang ◽  
Sizhe Dong ◽  
Hong-Li Wang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Effective Means ◽  
Computer Aided Diagnosis ◽  
Test Results ◽  
Back Propagation Algorithm ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.

Sign in / Sign up

Export Citation Format

Share Document