Computer‐aided mammogram diagnosis system using deep learning convolutional fully complex‐valued relaxation neural network classifier

2017 ◽  
Vol 11 (8) ◽  
pp. 656-662 ◽  
Author(s):  
Saraswathi Duraisamy ◽  
Srinivasan Emperumal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Classifier ◽  
Diagnosis System ◽  
Computer Aided ◽  
Complex Valued

A Framework for Extractive Text Summarization based on Deep Learning Modified Neural Network Classifier

2020 ◽  
Author(s):  
BalaAnand Muthu ◽  
Sivaparthipan CB ◽  
Priyan Malarvizhi Kumar ◽  
Seifedine Nimer Kadry ◽  
Ching-Hsien Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Summarization ◽  
Neural Network Classifier

Follow the Sound of Children’s Heart: A Deep-Learning-Based Computer-Aided Pediatric CHDs Diagnosis System

2020 ◽  
Vol 7 (3) ◽  
pp. 1994-2004 ◽  
Author(s):  
Bin Xiao ◽  
Yunqiu Xu ◽  
Xiuli Bi ◽  
Weisheng Li ◽  
Zhuo Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnosis System ◽  
Computer Aided

Computer-aided diagnosis system for breast ultrasound images using deep learning

2019 ◽  
Vol 64 (23) ◽  
pp. 235013 ◽  
Author(s):  
Hiroki Tanaka ◽  
Shih-Wei Chiu ◽  
Takanori Watanabe ◽  
Setsuko Kaoku ◽  
Takuhiro Yamaguchi
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

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