Abstract
The new type of Coronavirus (COVID-19), which started in 2019 in Wuhan, China, is an infectious virus that causes respiratory tract infection. This virus became effective in the world in a short time and turned into an epidemic. Early diagnosis of such infectious diseases and initiation of the necessary treatment at an early stage are very important. The use of X-ray (X-Ray) and Computed Tomography (CT) medical radiological imaging methods and deep learning and machine learning techniques help in the accurate and rapid detection of this disease. In this study; Two different datasets were used, including X-Ray images labeled normal-COVID-19-pneumonia (pneumonia) and CT images labeled normal-COVID-19. Inception ResNetV2, VGG-16 and DenseNet121 deep learning architectures and kNN and SVM classifiers are used. In this context, 3 different experiments were carried out. First of all, the classification performance of each network was examined. Then, the feature vectors produced by the networks were separately processed with classifiers. Finally, the feature vectors produced by the networks were combined and the classification process was carried out. As a result, the highest result for COVID-19 and normal images in the chest CT dataset was obtained with the combined features and kNN classifier with 98.9% accuracy.
References
- Bozkurt, F. "Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti", Avrupa Bilim ve Teknoloji Dergisi, no. 24, Apri 2021, pp. 149-156, https://doi:10.31590/ejosat.898385
- Erdaş, Ç. B., Detection and differentiation of COVID-19 using deep learning approach fed by x-rays, International Journal of Applied Mathematics, 8(3), 2020, pp. 097-101. https://dergipark.org.tr/en/download/article-file/1308359
- Özbay, E., Özbay, F. A., COVID-19 Detection from CT images with Deep Learning and Classification Approaches, DÜMF Mühendislik Dergisi 12(2), 2021, pp. 211-219. https://dergipark.org.tr/tr/download/article-file/1352635
- Kutlu, Y., Camgözlü, Y., Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural Networks, Natural and Engineering Sciences, 6(1), 2021, pp. 60-74. https://doi.org/10.28978/nesciences.868087
- Güraksın, G. E., COVID-19 Diagnosis Using Deep Learning, Düzce University Journal of Science & Technology, 9, 2021, pp. 8-23. https://doi.org/10.29130/dubited.866124
- Hemdan, E. E., A. Shouman, M., Karar, M.E., COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images, Cornel University Electrical Engineering and Systems Science -Image and Video Processing, https://doi.org/10.48550/arXiv.2003.11055
- Jia, G., Lam, H. K., Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method, Computers in Biology and Medicine, 134, 2921, pp. 104425. https://doi.org/10.1016/j.compbiomed.2021.104425
- Şahinbaş, K., Çatak, F. Ö., Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images, Data Science for COVID-19, 2021, pp. 451-466. https://doi.org/10.1016/B978-0-12-824536-1.00003-4
- Sethy, P.K., Behera, S.K., Detection of Coronavirus Disease (COVID-19) Based on Deep Features, Preprints, 2020, 2020030300, https://doi.org/10.20944/preprints202003.0300.v1
- Asnaoui, K., Chawki, Y., Using X-ray images and deep learning for automated detection of coronavirus disease, Journal of Biomolecular Structure and Dynamics, 39(10), 2021, pp. 3615-3626. https://doi.org/10.1080/07391102.2020.1767212
- Ardakani, A.A., Kanafi, A. R., Acharya,U. R. , Khadem,N. ,Mohammadi, A., Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural Networks, Computers in Biology and Medicine, 121, 20220, pp. 103795. https://doi.org/10.1016/j.compbiomed.2020.103795
- Kart, Ö., Başçiftçi, F., Makine Öğrenmesi Algoritmalarıyla Akciğer Tomografi Görüntülerinden COVID-19 Tespiti, Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, 28, 2021, pp. 630-637. https://doi.org/10.31590/ejosat.1009611
- https://www.kaggle.com/datasets/tawsifurrahman/ COVID19-radiography-database
- https://www.kaggle.com/datasets/ plameneduardo/sarscov2-ctscan-dataset
- K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, ArXiv Prepr., 2014, pp. 1409e1556
- https://www.researchgate.net/figure/Schematic-diagram-of-InceptionResNetV2-model-compressed-view_fig9_326421398
- Huang, G. Liu, Z. Maaten L. Van Der, Weinberger, K.Q. Densely connected convolutional networks,in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700e4708
Abstract
2019 yılında Çin’in Wuhan kentinde başlayan yeni tip Koronavirüs (COVID-19), solunum yolu enfeksiyonuna neden olan bulaşıcı bir virüstür. Bu virüs dünyada kısa sürede etkili olmuş ve bir salgına dönüşmüştür. Bu tür bulaşıcı hastalıkların erken teşhisi ve gerekli tedavinin erken süreçte başlatılması çok önemlidir. X-ışını (X-Ray) ve Bilgisayarlı Tomografi (BT) tıbbi radyolojik görüntüleme yöntemleri ile derin öğrenme ve makine öğrenmesi tekniklerinin kullanılması bu hastalığın doğru ve hızlı tespitine yardımcı olmaktadır. Bu çalışmada; normal-COVID-19-pnömoni (zatürre) etiketli X-Ray ve normal-COVID-19 etiketli BT görüntülerini içeren 2 farklı veri kümesi kullanılmıştır. Bununla birlikte; InceptionResNetV2, VGG-16 ve DenseNet121 derin öğrenme mimarileri ve kNN ile SVM sınıflandırıcıları kullanılmıştır. Bu kapsamda 3 farklı çalışma yürütülmüştür. Öncelikle her bir ağın sınıflandırma başarımı incelenmiştir. Daha sonra ağların ürettiği öznitelik vektörleri ayrı olarak sınıflandırıcılarla işleme sokulmuştur. Son olarak ağların ürettiği öznitelik vektörleri birleştirilmiş ve sınıflandırma işlemi gerçekleştirilmiştir. Sonuç olarak göğüs BT veri kümesindeki COVID-19 ve normal görüntüleri için en yüksek sonuç %98,9 doğruluk ile birleştirilmiş öznitelikler ve kNN sınıflandırıcısı ile elde edilmiştir.
References
- Bozkurt, F. "Derin Öğrenme Tekniklerini Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti", Avrupa Bilim ve Teknoloji Dergisi, no. 24, Apri 2021, pp. 149-156, https://doi:10.31590/ejosat.898385
- Erdaş, Ç. B., Detection and differentiation of COVID-19 using deep learning approach fed by x-rays, International Journal of Applied Mathematics, 8(3), 2020, pp. 097-101. https://dergipark.org.tr/en/download/article-file/1308359
- Özbay, E., Özbay, F. A., COVID-19 Detection from CT images with Deep Learning and Classification Approaches, DÜMF Mühendislik Dergisi 12(2), 2021, pp. 211-219. https://dergipark.org.tr/tr/download/article-file/1352635
- Kutlu, Y., Camgözlü, Y., Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural Networks, Natural and Engineering Sciences, 6(1), 2021, pp. 60-74. https://doi.org/10.28978/nesciences.868087
- Güraksın, G. E., COVID-19 Diagnosis Using Deep Learning, Düzce University Journal of Science & Technology, 9, 2021, pp. 8-23. https://doi.org/10.29130/dubited.866124
- Hemdan, E. E., A. Shouman, M., Karar, M.E., COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images, Cornel University Electrical Engineering and Systems Science -Image and Video Processing, https://doi.org/10.48550/arXiv.2003.11055
- Jia, G., Lam, H. K., Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method, Computers in Biology and Medicine, 134, 2921, pp. 104425. https://doi.org/10.1016/j.compbiomed.2021.104425
- Şahinbaş, K., Çatak, F. Ö., Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images, Data Science for COVID-19, 2021, pp. 451-466. https://doi.org/10.1016/B978-0-12-824536-1.00003-4
- Sethy, P.K., Behera, S.K., Detection of Coronavirus Disease (COVID-19) Based on Deep Features, Preprints, 2020, 2020030300, https://doi.org/10.20944/preprints202003.0300.v1
- Asnaoui, K., Chawki, Y., Using X-ray images and deep learning for automated detection of coronavirus disease, Journal of Biomolecular Structure and Dynamics, 39(10), 2021, pp. 3615-3626. https://doi.org/10.1080/07391102.2020.1767212
- Ardakani, A.A., Kanafi, A. R., Acharya,U. R. , Khadem,N. ,Mohammadi, A., Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural Networks, Computers in Biology and Medicine, 121, 20220, pp. 103795. https://doi.org/10.1016/j.compbiomed.2020.103795
- Kart, Ö., Başçiftçi, F., Makine Öğrenmesi Algoritmalarıyla Akciğer Tomografi Görüntülerinden COVID-19 Tespiti, Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, 28, 2021, pp. 630-637. https://doi.org/10.31590/ejosat.1009611
- https://www.kaggle.com/datasets/tawsifurrahman/ COVID19-radiography-database
- https://www.kaggle.com/datasets/ plameneduardo/sarscov2-ctscan-dataset
- K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, ArXiv Prepr., 2014, pp. 1409e1556
- https://www.researchgate.net/figure/Schematic-diagram-of-InceptionResNetV2-model-compressed-view_fig9_326421398
- Huang, G. Liu, Z. Maaten L. Van Der, Weinberger, K.Q. Densely connected convolutional networks,in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700e4708
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler(Araştırma) |
| Authors | |
| Early Pub Date | October 22, 2023 |
| Publication Date | November 20, 2023 |
| Published in Issue | Year 2023 Volume: 16 Issue: 2 |
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