scholarly journals An Efficient Image Based Feature Extraction and Feature Selection Model for Medical Data Clustering Using Deep Neural Networks

2021 ◽  
Vol 38 (4) ◽  
pp. 1141-1148
Author(s):  
Mohammed Zaheer Ahmed ◽  
Chitraivel Mahesh
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Data Clustering ◽  
Deep Neural Networks ◽  
Medical Data ◽  
Selection Model

scholarly journals An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

2019 ◽  
Vol 9 (14) ◽  
pp. 2921 ◽  
Author(s):  
Siti Nurmaini ◽  
Radiyati Umi Partan ◽  
Wahyu Caesarendra ◽  
Tresna Dewi ◽  
Muhammad Naufal Rahmatullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Machine Learning Technique ◽  
Proposed Model ◽  
Learning Technique ◽  
Processing Steps ◽  
Unsupervised Feature Extraction

An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

Deep-gKnock: Nonlinear group-feature selection with deep neural networks

2021 ◽  
Vol 135 ◽  
pp. 139-147
Author(s):  
Guangyu Zhu ◽  
Tingting Zhao
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Neural Networks

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

2021 ◽  
Author(s):  
Jafar A. Alzubi ◽  
Omar A. Alzubi ◽  
Majdi Beseiso ◽  
Anil Kumar Budati ◽  
K. Shankar
Keyword(s):  
Neural Networks ◽  
Data Transmission ◽  
Deep Neural Networks ◽  
Homomorphic Encryption ◽  
Medical Data

Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection

2016 ◽  
Vol 72 ◽  
pp. 1-11 ◽  
Author(s):  
Sebastian Pölsterl ◽  
Sailesh Conjeti ◽  
Nassir Navab ◽  
Amin Katouzian
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Survival Analysis ◽  
Medical Data ◽  
High Dimensional

scholarly journals Feature selection using Deep Neural Networks

2015 ◽  
Author(s):  
Debaditya Roy ◽  
K. Sri Rama Murty ◽  
C. Krishna Mohan
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Neural Networks

scholarly journals Modular Dynamic Neural Network: A Continual Learning Architecture

2021 ◽  
Vol 11 (24) ◽  
pp. 12078
Author(s):  
Daniel Turner ◽  
Pedro J. S. Cardoso ◽  
João M. F. Rodrigues
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Simple Task ◽  
Dynamic Neural Network ◽  
Main Components ◽  
Over Time ◽  
Continual Learning

Learning to recognize a new object after having learned to recognize other objects may be a simple task for a human, but not for machines. The present go-to approaches for teaching a machine to recognize a set of objects are based on the use of deep neural networks (DNN). So, intuitively, the solution for teaching new objects on the fly to a machine should be DNN. The problem is that the trained DNN weights used to classify the initial set of objects are extremely fragile, meaning that any change to those weights can severely damage the capacity to perform the initial recognitions; this phenomenon is known as catastrophic forgetting (CF). This paper presents a new (DNN) continual learning (CL) architecture that can deal with CF, the modular dynamic neural network (MDNN). The presented architecture consists of two main components: (a) the ResNet50-based feature extraction component as the backbone; and (b) the modular dynamic classification component, which consists of multiple sub-networks and progressively builds itself up in a tree-like structure that rearranges itself as it learns over time in such a way that each sub-network can function independently. The main contribution of the paper is a new architecture that is strongly based on its modular dynamic training feature. This modular structure allows for new classes to be added while only altering specific sub-networks in such a way that previously known classes are not forgotten. Tests on the CORe50 dataset showed results above the state of the art for CL architectures.

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