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Derin Evrişimli Sinir Ağları Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti

Yıl 2021, Cilt: 4 Sayı: 2, 1 - 8, 19.08.2021

Öz

COVID-19 salgını, dünya çapında büyük bir güvenlik ve sağlık tehdidi haline gelmiştir. Tipik akciğer X-ray görüntüleri şüpheli vakaların erken taranmasına yardımcı olabilse de, çeşitli viral pnömoni (zatürre) görüntüleri COVID-19 ile benzerdir ve benzer özellikler içermektedir. Dolayısıyla radyologların diğer benzer akciğer hastalıklarını COVID-19’dan ayırt etmesi zordur. Bu bağlamda, COVID-19 semptomlarının viral pnömoniye benzer olması, yanlış tanılara yol açabilmektedir. Bu çalışmada akciğer X-ray görüntülerinden COVID-19’un derin evrişimli sinir ağları (ESA) kullanılarak tespiti yapılmıştır. Çalışmada bir derin ESA modeli sunulmuş olup, açık erişimli veri kümesi üzerinde deneysel çalışmalar gerçekleştirilmiştir. Bu veri kümesinde, COVID-19, Normal ve Viral Pnömoni olmak üzere üç sınıfa ait toplam 3886 görüntü bulunmaktadır. Bu veri kümesini kullanarak, önerilen ESA modelinin doğruluğu üzerine performans değerlendirmeleri yapılmış olup ayrıca sonuçlar VGG19, Inception V3 ve ResNet50 gibi derin ESA tabanlı diğer popüler modeller ile karşılaştırılmıştır. Deneysel çalışmalarda, önerilen derin ESA modeli ile doğruluk değeri en yüksek %96 bulunmuştur.

Kaynakça

  • [1] Organization W. H., "Global COVID-19 report," March 25,2020 2020.
  • [2] Medicine J. H. U., Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), accessed on Jan. 2021. [Online]. Available: https://coronavirus.jhu.edu/map.html
  • [3] News A. J., India's poor testing rate may have masked coronavirus cases. accessed on Mar. 2020. [Online]. Available: https://www.aljazeera.com/news/2020/3/18/indias-poor-testing-rate-may-have-masked-coronavirus-cases
  • [4] Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., ... & Islam, M. T., “Can AI help in screening viral and COVID-19 pneumonia?”, IEEE Access, 8, 132665-132676, 2020.
  • [5] Mangal, A., Kalia, S., Rajgopal, H., Rangarajan, K., Namboodiri, V., Banerjee, S., & Arora, C., “CovidAID: COVID-19 detection using chest X-ray”, arXiv preprint arXiv:2004.09803, 2020.
  • [6] El Asnaoui, K., & Chawki, Y., “Using X-ray images and deep learning for automated detection of coronavirus disease”, Journal of Biomolecular Structure and Dynamics, 1-12, 2020.
  • [7] Apostolopoulos, I. D., & Mpesiana, T. A., “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks”, Physical and Engineering Sciences in Medicine, 43(2), 635-640, 2020.
  • [8] Abbas, A., Abdelsamea, M. M., & Gaber, M. M., “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network”, Applied Intelligence, 51(2), 854-864, 2021.
  • [9] Rahimzadeh, M., & Attar, A., “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2”, Informatics in Medicine Unlocked, 19, 100360, 2020.
  • [10] Narin, A., Kaya, C., & Pamuk, Z., “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks”, arXiv preprint arXiv:2003.10849, 2020.
  • [11] Majeed, T., Rashid, R., Ali, D., & Asaad, A., “Covid-19 detection using cnn transfer learning from x-ray images”, medRxiv, 2020.
  • [12] Rafi, T. H., “An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images”, In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-5). IEEE, 2020.
  • [13] Hemdan, E. E. D., Shouman, M. A., & Karar, M. E., “Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images”, arXiv preprint arXiv:2003.11055, 2020.
  • [14] Wang, L., Lin, Z. Q., & Wong, A., “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images”, Scientific Reports, 10(1), 1-12, 2020.
  • [15] T. R. Muhammad E. H. Chowdhury, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar R. Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al-Emadi, Mamun Bin Ibne Reaz. (2020), Covid-19 Chest X-Ray Database, Available: https://www.kaggle.com/tawsifurrahman/covid19- radiography-database
  • [16] Oğuz Ç. & Yağanoğlu, M., “Determination of Covid-19 Possible Cases by Using Deep Learning Techniques”, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 7-17, 2021.
  • [17] Krizhevsky, A., Sutskever, I., & Hinton, G. E., “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems, 25, 1097-1105, 2012.
  • [18] Meunier, L. C. V., & Chandy, D. A., “Design of convolution neural network for facial emotion recognition”, In 2019 2nd International Conference on Signal Processing and Communication (ICSPC), pp. 376-379, IEEE, 2019.
  • [19] Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G. S., “COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images”, Journal of Ambient Intelligence and Humanized Computing, 1-13, 2021.
  • [20]Uçkun F. A., Özer H., Nurbaş E., and Onat E., "Direction Finding Using Convolutional Neural Networks and Convolutional Recurrent Neural Networks", 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, pp. 1-4, 2020.
  • [21] Candan, H., Durmuş, A., & Harman, G. Genetik Algoritma Ve Sınıflandırıcı Yöntemler İle Kanser Tahmini. Veri Bilimi, 2(1), 30-34.
  • [22] Bozkurt, F., Çoban, Ö., Baturalp Günay, F., & Yücel Altay, Ş., “High Performance Twitter Sentiment Analysis Using CUDA Based Distance Kernel on GPUs”, Tehnički vjesnik, 26(5), 1218-1227, 2019
Yıl 2021, Cilt: 4 Sayı: 2, 1 - 8, 19.08.2021

Öz

Kaynakça

  • [1] Organization W. H., "Global COVID-19 report," March 25,2020 2020.
  • [2] Medicine J. H. U., Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), accessed on Jan. 2021. [Online]. Available: https://coronavirus.jhu.edu/map.html
  • [3] News A. J., India's poor testing rate may have masked coronavirus cases. accessed on Mar. 2020. [Online]. Available: https://www.aljazeera.com/news/2020/3/18/indias-poor-testing-rate-may-have-masked-coronavirus-cases
  • [4] Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., ... & Islam, M. T., “Can AI help in screening viral and COVID-19 pneumonia?”, IEEE Access, 8, 132665-132676, 2020.
  • [5] Mangal, A., Kalia, S., Rajgopal, H., Rangarajan, K., Namboodiri, V., Banerjee, S., & Arora, C., “CovidAID: COVID-19 detection using chest X-ray”, arXiv preprint arXiv:2004.09803, 2020.
  • [6] El Asnaoui, K., & Chawki, Y., “Using X-ray images and deep learning for automated detection of coronavirus disease”, Journal of Biomolecular Structure and Dynamics, 1-12, 2020.
  • [7] Apostolopoulos, I. D., & Mpesiana, T. A., “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks”, Physical and Engineering Sciences in Medicine, 43(2), 635-640, 2020.
  • [8] Abbas, A., Abdelsamea, M. M., & Gaber, M. M., “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network”, Applied Intelligence, 51(2), 854-864, 2021.
  • [9] Rahimzadeh, M., & Attar, A., “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2”, Informatics in Medicine Unlocked, 19, 100360, 2020.
  • [10] Narin, A., Kaya, C., & Pamuk, Z., “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks”, arXiv preprint arXiv:2003.10849, 2020.
  • [11] Majeed, T., Rashid, R., Ali, D., & Asaad, A., “Covid-19 detection using cnn transfer learning from x-ray images”, medRxiv, 2020.
  • [12] Rafi, T. H., “An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images”, In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-5). IEEE, 2020.
  • [13] Hemdan, E. E. D., Shouman, M. A., & Karar, M. E., “Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images”, arXiv preprint arXiv:2003.11055, 2020.
  • [14] Wang, L., Lin, Z. Q., & Wong, A., “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images”, Scientific Reports, 10(1), 1-12, 2020.
  • [15] T. R. Muhammad E. H. Chowdhury, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar R. Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al-Emadi, Mamun Bin Ibne Reaz. (2020), Covid-19 Chest X-Ray Database, Available: https://www.kaggle.com/tawsifurrahman/covid19- radiography-database
  • [16] Oğuz Ç. & Yağanoğlu, M., “Determination of Covid-19 Possible Cases by Using Deep Learning Techniques”, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 7-17, 2021.
  • [17] Krizhevsky, A., Sutskever, I., & Hinton, G. E., “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems, 25, 1097-1105, 2012.
  • [18] Meunier, L. C. V., & Chandy, D. A., “Design of convolution neural network for facial emotion recognition”, In 2019 2nd International Conference on Signal Processing and Communication (ICSPC), pp. 376-379, IEEE, 2019.
  • [19] Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G. S., “COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images”, Journal of Ambient Intelligence and Humanized Computing, 1-13, 2021.
  • [20]Uçkun F. A., Özer H., Nurbaş E., and Onat E., "Direction Finding Using Convolutional Neural Networks and Convolutional Recurrent Neural Networks", 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, pp. 1-4, 2020.
  • [21] Candan, H., Durmuş, A., & Harman, G. Genetik Algoritma Ve Sınıflandırıcı Yöntemler İle Kanser Tahmini. Veri Bilimi, 2(1), 30-34.
  • [22] Bozkurt, F., Çoban, Ö., Baturalp Günay, F., & Yücel Altay, Ş., “High Performance Twitter Sentiment Analysis Using CUDA Based Distance Kernel on GPUs”, Tehnički vjesnik, 26(5), 1218-1227, 2019
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ferhat Bozkurt 0000-0003-0088-5825

Mete Yağanoğlu 0000-0003-3045-169X

Yayımlanma Tarihi 19 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA Bozkurt, F., & Yağanoğlu, M. (2021). Derin Evrişimli Sinir Ağları Kullanarak Akciğer X-Ray Görüntülerinden COVID-19 Tespiti. Veri Bilimi, 4(2), 1-8.



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