Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach

2021 ◽  
pp. 102825
Author(s):  
Debendra Muduli ◽  
Ratnakar Dash ◽  
Banshidhar Majhi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolution Neural Network ◽  
Automated Diagnosis ◽  
Deep Convolution Neural Network

Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms

2020 ◽  
Vol 79 (25-26) ◽  
pp. 18447-18479 ◽  
Author(s):  
Ghulam Murtaza ◽  
Liyana Shuib ◽  
Ainuddin Wahid Abdul Wahab ◽  
Ghulam Mujtaba ◽  
Ghulam Raza
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Classification ◽  
Convolution Neural Network ◽  
Breast Cancer Classification ◽  
Deep Convolution Neural Network

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

Sign in / Sign up

Export Citation Format

Share Document