COVID-19 detection using X-ray images and statistical measurements
| dc.contributor.author | Avuçlu, Emre | |
| dc.date.accessioned | 2023-01-12T05:49:53Z | |
| dc.date.available | 2023-01-12T05:49:53Z | |
| dc.date.issued | 2022 | |
| dc.department | Teknik Bilimler Meslek YĂĽksekokulu | |
| dc.description.abstract | The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features. | |
| dc.identifier.doi | 10.1016/j.measurement.2022.111702 | |
| dc.identifier.endpage | - | en_US |
| dc.identifier.issn | 0263-2241 | |
| dc.identifier.issue | - | en_US |
| dc.identifier.pmid | 35942188 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | - | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.1016/j.measurement.2022.111702 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/9882 | |
| dc.identifier.volume | 201 | en_US |
| dc.identifier.wos | WOS:000865021800004 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.publisher | Elsevier B.V. | |
| dc.relation.ispartof | Measurement: Journal of the International Measurement Confederation | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Biomedical Images | |
| dc.subject | COVID-19 | |
| dc.subject | Feature Extraction | |
| dc.subject | Machine Learning Algorithms | |
| dc.title | COVID-19 detection using X-ray images and statistical measurements | |
| dc.type | Article |
