Determination of Covid-19 Possible Cases by Using Deep Learning Techniques
A large number of cases have been identified in the world with the emergence of COVID-19 and the rapid spread of the virus. Thousands of people have died due to COVID-19. This very spreading virus may result in serious consequnces including pneumonia, kidney failure acute respiratory infection. It...
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Sakarya University
2021-02-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/1218061 |
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| author | Mete Yağanoğlu Çinare Oğuz |
| author_facet | Mete Yağanoğlu Çinare Oğuz |
| author_sort | Mete Yağanoğlu |
| collection | DOAJ |
| description | A large number of cases have been identified in the world with the emergence of COVID-19 and the rapid spread of the virus. Thousands of people have died due to COVID-19. This very spreading virus may result in serious consequnces including pneumonia, kidney failure acute respiratory infection. It can even cause death in severe cases. Therefore, early diagnosis is vital. Due to the limited number of COVID-19 test kits, one of the first diagnostic techniques in suspected COVID-19 patients is to have Thorax Computed Tomography (CT) applied to individuals with suspected COVID-19 cases when it is not possible to administer these test kits. In this study, it was aimed to analyze the CT images automatically and to direct probable COVID-19 cases to PCR test quickly in order to make quick controls and ease the burden of healthcare workers. ResNet-50 and Alexnet deep learning techniques were used in the extraction of deep features. Their performance was measured using Support Vector Machines (SVM), Nearest neighbor algorithm (KNN), Linear Discrimination Analysis (LDA), Decision trees, Random forest (RF) and Naive Bayes methods as the methods of classification. The best results were obtained with ResNet-50 and SVM classification methods. The success rate was found as 95.18%. |
| format | Article |
| id | doaj-art-2bc31c796d494bb1b214712f2cbbed7b |
| institution | Kabale University |
| issn | 2147-835X |
| language | English |
| publishDate | 2021-02-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-2bc31c796d494bb1b214712f2cbbed7b2024-12-23T08:07:40ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2021-02-0125111110.16984/saufenbilder.77443528Determination of Covid-19 Possible Cases by Using Deep Learning TechniquesMete Yağanoğlu0https://orcid.org/0000-0003-3045-169XÇinare Oğuz1https://orcid.org/0000-0003-0410-2429ATATURK UNIVERSITYATATURK UNIVERSITYA large number of cases have been identified in the world with the emergence of COVID-19 and the rapid spread of the virus. Thousands of people have died due to COVID-19. This very spreading virus may result in serious consequnces including pneumonia, kidney failure acute respiratory infection. It can even cause death in severe cases. Therefore, early diagnosis is vital. Due to the limited number of COVID-19 test kits, one of the first diagnostic techniques in suspected COVID-19 patients is to have Thorax Computed Tomography (CT) applied to individuals with suspected COVID-19 cases when it is not possible to administer these test kits. In this study, it was aimed to analyze the CT images automatically and to direct probable COVID-19 cases to PCR test quickly in order to make quick controls and ease the burden of healthcare workers. ResNet-50 and Alexnet deep learning techniques were used in the extraction of deep features. Their performance was measured using Support Vector Machines (SVM), Nearest neighbor algorithm (KNN), Linear Discrimination Analysis (LDA), Decision trees, Random forest (RF) and Naive Bayes methods as the methods of classification. The best results were obtained with ResNet-50 and SVM classification methods. The success rate was found as 95.18%.https://dergipark.org.tr/tr/download/article-file/1218061resnet-50alexnetdeep learningcovid-19classification |
| spellingShingle | Mete Yağanoğlu Çinare Oğuz Determination of Covid-19 Possible Cases by Using Deep Learning Techniques Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi resnet-50 alexnet deep learning covid-19 classification |
| title | Determination of Covid-19 Possible Cases by Using Deep Learning Techniques |
| title_full | Determination of Covid-19 Possible Cases by Using Deep Learning Techniques |
| title_fullStr | Determination of Covid-19 Possible Cases by Using Deep Learning Techniques |
| title_full_unstemmed | Determination of Covid-19 Possible Cases by Using Deep Learning Techniques |
| title_short | Determination of Covid-19 Possible Cases by Using Deep Learning Techniques |
| title_sort | determination of covid 19 possible cases by using deep learning techniques |
| topic | resnet-50 alexnet deep learning covid-19 classification |
| url | https://dergipark.org.tr/tr/download/article-file/1218061 |
| work_keys_str_mv | AT meteyaganoglu determinationofcovid19possiblecasesbyusingdeeplearningtechniques AT cinareoguz determinationofcovid19possiblecasesbyusingdeeplearningtechniques |