Literature DB >> 25214377

Detection of Pneumonia in chest X-ray images.

N Ravia Shabnam Parveen1, M Mohamed Sathik2.   

Abstract

Pneumonia is the common type of infection found in the world. The infection spreads in the lungs area of a human body. The chest x-ray is performed to diagnose this infection. Physicians use this X-ray image to diagnose or monitor treatment for conditions of pneumonia. This type of chest X-ray is also used in the diagnosis of diseases like emphysema, lung cancer, line and tube placement and tuberculosis. Feature extraction methods like DWT, WFT, and WPT can also be used. In this paper, detection of pneumonia infection by unsupervised fuzzy c-means classification learning algorithm is used. This approach gives better result than the rest of the methods. In fuzzy c-means, each resultant pixel gives accurate value since it has a weight associated with it.

Entities:  

Keywords:  Feature extraction; fuzzy c-means classification; pneumonia; unsupervised learning

Mesh:

Year:  2011        PMID: 25214377     DOI: 10.3233/XST-2011-0304

Source DB:  PubMed          Journal:  J Xray Sci Technol        ISSN: 0895-3996            Impact factor:   1.535


  6 in total

1.  Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset.

Authors:  Kadir Aktas; Vuk Ignjatovic; Dragan Ilic; Marina Marjanovic; Gholamreza Anbarjafari
Journal:  Signal Image Video Process       Date:  2022-07-20       Impact factor: 1.583

2.  Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter.

Authors:  Wan Siti Halimatul Munirah Wan Ahmad; W Mimi Diyana W Zaki; Mohammad Faizal Ahmad Fauzi
Journal:  Biomed Eng Online       Date:  2015-03-04       Impact factor: 2.819

Review 3.  Computer-aided detection in chest radiography based on artificial intelligence: a survey.

Authors:  Chunli Qin; Demin Yao; Yonghong Shi; Zhijian Song
Journal:  Biomed Eng Online       Date:  2018-08-22       Impact factor: 2.819

4.  Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images.

Authors:  Sheetal Rajpal; Navin Lakhyani; Ayush Kumar Singh; Rishav Kohli; Naveen Kumar
Journal:  Chaos Solitons Fractals       Date:  2021-02-10       Impact factor: 5.944

5.  Ensemble method using real images, metadata and synthetic images for control of class imbalance in classification.

Authors:  Rogers Aloo; Atsuko Mutoh; Koichi Moriyama; Tohgoroh Matsui; Nobuhiro Inuzuka
Journal:  Artif Life Robot       Date:  2022-09-02

6.  Automated quantification of COVID-19 severity and progression using chest CT images.

Authors:  Jiantao Pu; Joseph K Leader; Andriy Bandos; Shi Ke; Jing Wang; Junli Shi; Pang Du; Youmin Guo; Sally E Wenzel; Carl R Fuhrman; David O Wilson; Frank C Sciurba; Chenwang Jin
Journal:  Eur Radiol       Date:  2020-08-13       Impact factor: 5.315

  6 in total

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