Feature Extraction and Classification of X-Ray Lung Images Using Haralick Texture Features

2018 ◽  
pp. 899-907 ◽  
Author(s):  
N. Vamsha Deepa ◽  
Nanditha Krishna ◽  
G. Hemanth Kumar
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
X Ray

scholarly journals Classification of Coronavirus Images using Shrunken Features

2020 ◽  
Author(s):  
Saban Ozturk ◽  
Umut Ozkaya ◽  
Mucahid Barstugan
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Principal Component ◽  
Extraction Methods ◽  
X Ray ◽  
Effective Interventions ◽  
Long Duration ◽  
Clinical Diagnoses ◽  
Short Time

AbstractNecessary screenings must be performed to control the spread of the Corona Virus (COVID-19) in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.

New feature extraction method for classification of agricultural products from x-ray images

1999 ◽  
Author(s):  
Ashit Talukder ◽  
David P. Casasent ◽  
Ha-Woon Lee ◽  
Pamela M. Keagy ◽  
Thomas F. Schatzki
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Agricultural Products ◽  
X Ray ◽  
Feature Extraction Method ◽  
New Feature

Texture Feature Extraction and Classification of Pork Loin Ultrasonography Images

2013 ◽  
Vol 791-793 ◽  
pp. 1978-1981
Author(s):  
Tao Li ◽  
Jian Xun Zhang ◽  
Quan Sun
Keyword(s):  
Feature Extraction ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Texture Features ◽  
Fat Content ◽  
Image Processing Algorithm ◽  
Texture Feature Extraction ◽  
Pork Loin ◽  
Pork Fat

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

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