Detection of pneumonia in chest X‐ray images by using 2D discrete wavelet feature extraction with random forest

2020 ◽  
Author(s):  
Abdurrahim Akgundogdu
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Discrete Wavelet ◽  
X Ray ◽  
Chest X Ray

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

scholarly journals Applications of Discrete Wavelet Transform for Feature Extraction to Increase the Accuracy of Monitoring Systems of Liquid Petroleum Products

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3215
Author(s):  
Mohammed Balubaid ◽  
Mohammad Amir Sattari ◽  
Osman Taylan ◽  
Ahmed A. Bakhsh ◽  
Ehsan Nazemi
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Volume Ratio ◽  
Petroleum Products ◽  
Discrete Wavelet ◽  
X Ray ◽  
Network Training ◽  
Transport Code ◽  
Pass Through

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.

COVID-19 Detection Using Feature Extraction and Semi-Supervised Learning from Chest X-ray Images

2021 ◽  
Author(s):  
Samiul Haque ◽  
Mohammad Akidul Hoque ◽  
Mohammad Ariful Islam Khan ◽  
Sabbir Ahmed
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
X Ray ◽  
Chest X Ray

scholarly journals A Novel Machine Learning Approach for Tuberculosis Segmentation and Prediction Using Chest-X-Ray (CXR) Images

2021 ◽  
Vol 11 (19) ◽  
pp. 9057
Author(s):  
Xavier Alphonse Inbaraj ◽  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Feature Extraction ◽  
Decision Tree ◽  
High Morbidity ◽  
Decision Tree Classifier ◽  
X Ray ◽  
Poor Countries ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Novel Method

Tuberculosis is a potential fatal disease with high morbidity and mortality rates. Tuberculosis death rates are rising, posing a serious health threat in several poor countries around the world. To address this issue, we proposed a novel method for detecting tuberculosis in chest X-ray (CXR) images that uses a three-phased approach to distinguish tuberculosis such as segmentation, feature extraction, and classification. In a CXR, we utilized the Weiner filter to distinguish and reduce the impulse noise. The features were extracted from CXR images and trained using a decision tree classifier known as the stacked loopy decision tree (SLDT) classifier. For the classification process, the ROI-based morphological approach was applied in the mentioned three-phased approach, and the feature extraction was accomplished through chromatic and Prewitt-edge highlights.

scholarly journals COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function

2021 ◽  
Vol 21 (1) ◽  
pp. 31-42
Author(s):  
I Gusti Ayu Agung Diatri Indradewi ◽  
Ni Wayan Sumartini Saraswati ◽  
NI Wayan Wardani
Keyword(s):  
Feature Extraction ◽  
Error Rate ◽  
Haar Wavelet ◽  
Quality Parameters ◽  
Classification Model ◽  
Support Vector ◽  
Daubechies Wavelet ◽  
X Ray ◽  
Chest X Ray ◽  
Parameters Of Accuracy

Our previous work regarding the X-Ray detection of COVID-19 using Haar wavelet feature extraction and the Support Vector Machines (SVM) classification machine has shown that the combination of the two methods can detect COVID-19 well but then the question arises whether the Haar wavelet is the best wavelet method. So that in this study we conducted experiments on several wavelet methods such as biorthogonal, coiflet, Daubechies, haar, and symlets for chest X-Ray feature extraction with the same dataset. The results of the feature extraction are then classified using SVM and measure the quality of the classification model with parameters of accuracy, error rate, recall, specification, and precision. The results showed that the Daubechies wavelet gave the best performance for all classification quality parameters. The Daubechies wavelet transformation gave 95.47% accuracy, 4.53% error rate, 98.75% recall, 92.19% specificity, and 93.45% precision.

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