scholarly journals The Feature Extraction for Coronavirus Disease Classification Techniques

2021 ◽  
Author(s):  
Anis Fitri Nur Masruriyah ◽  
Hasan Basri ◽  
Hanny Hikmayanti Handayani ◽  
Ahmad Fauzi
Keyword(s):  
Feature Extraction ◽  
Disease Classification ◽  
Classification Methods ◽  
Extraction Techniques ◽  
Comparison Result ◽  
Early Symptoms ◽  
Number Of Patients ◽  
Improve Accuracy ◽  
Long Time

Abstract COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore confirmed by the swab and CXR methods. The process of swab and CXR takes a relatively long time since in CXR some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with feature extraction techniques and classification methods. The result of this study capable to identify CXR with COVID-19 and an accuracy of 96.5%. In addition, this study even compares the classification results without using feature extraction techniques. The comparison result showed that feature extraction was able to significantly improve accuracy.

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

Comparison of feature extraction techniques for classification of hardwood species

2018 ◽  
Vol 4 (2/3) ◽  
pp. 106
Author(s):  
Arvind R. Yadav ◽  
R.S. Anand ◽  
M.L. Dewal ◽  
Sangeeta Gupta ◽  
Jayendra Kumar
Keyword(s):  
Feature Extraction ◽  
Extraction Techniques ◽  
Hardwood Species

scholarly journals Detection and Classification of R-Peak Using Naïve Bayes Classifier

2018 ◽  
Vol 7 (3.27) ◽  
pp. 397 ◽  
Author(s):  
S Celin ◽  
K Vasanth
Keyword(s):  
Feature Extraction ◽  
Performance Measures ◽  
Heart Diseases ◽  
Processing Technique ◽  
Challenging Problem ◽  
Bayes Classifier ◽  
Extraction Techniques ◽  
And Performance ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and performance measures are presented in the pre-processing technique. And also the classification of ECG, analysis of input beat selection and the output of classifiers are also discussed in this paper.  

A Structured Approach towards Robust Database Collection for Speaker Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 53 ◽  
Author(s):  
Pardeep Sangwan ◽  
Saurabh Bhardwaj
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Recognition System ◽  
Classification Methods ◽  
Extraction Techniques ◽  
Recognition Phase ◽  
Speech Database ◽  
Biometric Systems ◽  
Recognition Systems ◽  
Structured Approach

<p>Speaker recognition systems are classified according to their database, feature extraction techniques and classification methods. It is analyzed that there is a much need to work upon all the dimensions of forensic speaker recognition systems from the very beginning phase of database collection to recognition phase. The present work provides a structured approach towards developing a robust speech database collection for efficient speaker recognition system. The database required for both systems is entirely different. The databases for biometric systems are readily available while databases for forensic speaker recognition system are scarce. The paper also presents several databases available for speaker recognition systems.</p><p> </p>

Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species

2014 ◽  
Vol 18 (4) ◽  
pp. 783-797 ◽  
Author(s):  
Marco T. A. Rodrigues ◽  
Mário H. G. Freitas ◽  
Flávio L. C. Pádua ◽  
Rogério M. Gomes ◽  
Eduardo G. Carrano
Keyword(s):  
Feature Extraction ◽  
Fish Species ◽  
Automatic Classification ◽  
Cluster Detection ◽  
Extraction Techniques ◽  
Detection Algorithms

scholarly journals Performance Measures of Respiratory Disease Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 884-892
Author(s):  
G. Rama Janani
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Performance Measures ◽  
Respiratory Infections ◽  
Respiratory Illness ◽  
Extraction Methods ◽  
Disease Classification ◽  
Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN

2019 ◽  
Vol 8 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Suman Lata ◽  
Rakesh Kumar
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Cardiac Diseases ◽  
Ecg Signals ◽  
Processing Steps ◽  
Ann Classification ◽  
Selection Of

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.

Classification of Alzheimer’s from T2 Trans-Axial Brain MR Images

2012 ◽  
Vol 2 (3) ◽  
pp. 30-43 ◽  
Author(s):  
Namita Aggarwal ◽  
Bharti Rana ◽  
R. K. Agrawal
Keyword(s):  
Feature Extraction ◽  
Order Statistics ◽  
Second Order ◽  
Discrete Wavelet ◽  
Elderly Persons ◽  
Extraction Techniques ◽  
Mr Images ◽  
Second Order Statistics ◽  
Brain Mr Images

Alzheimer’s disease is the most common form of dementia occurring in the elderly persons. Its early diagnosis may help in providing proper treatment. To date, there is no appropriate technique available to automatically classify it using MR brain images. In this work, first-and-second-order-statistics (FSOS) was employed for classification of Alzheimer’s from T2 trans-axial brain MR images. Although FSOS is a simple and well known feature extraction technique, it is not yet explored for Alzheimer’s classification. Performance of FSOS was compared with the state-of-the-art feature extraction techniques. Five commonly used classifiers were employed to build decision models. The performance of the models was evaluated in terms of sensitivity, specificity, accuracy, F-measure, training, and testing time. These models were built with varying number of training samples. Results showed that FSOS outperforms all the other existing feature extraction techniques in terms of all the considered performance measures. This was also validated by a statistical test. Interestingly, it was found that FSOS gives high performance irrespective of the choice of classifier and it works well even on small available number of samples, which is usually desired for all real time problems.Keyword: Discrete Wavelet Transform, Feature Extraction, First and Second Order Statistics, Gabor Transform, Magnetic Resonance Imaging, Slantlet Transform

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