Dergiler / Natural and Engineering Sciences / 2021 / Cilt: 6 - Sayı: 1

Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks

Sayfa
60–74
Yayın Tarihi
2021-01-25
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:COVID-19 is an epidemic disease that seriously affects elderly people and patients with chronic diseases and causes deaths.
📊
Temel Bulgular & Veri:Fast and accurate early diagnosis has an important role.
💡
Sonuç & Katkı:As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Diagnosis · COVID-19 · Coronavirus

Abstract

COVID-19 is an epidemic disease that seriously affects elderly people and patients with chronic diseases and causes deaths. Fast and accurate early diagnosis has an important role. Although chest images obtained by computed tomography are accepted as a gold standard, problems are often encountered in accessing this device. For this reason, it is very important to diagnose with more accessible devices such as x-ray machines. These studies have been accelerated with deep neural network models and good results have been obtained. In this study, two different approach models are proposed for this purpose. At first study, training with the COVID-19 data set shared as open access and the test results with different classifiers. The other is the comparison of the results using a Pre-trained model MobileNet. COVID-19 patients, pneumonia patients and normal individuals were classified with 99.53% accuracy by the designed CNN with SVM model which was trained with the COVID-19 data set. As a result, because X-rays are a special type of image, a CNN model trained with X-ray images would be a good choice rather than using pre-trained deep networks with different images. As a result, since X-rays are a special type of picture, it was seen that a CNN model trained with X-ray images should be a better choice, rather than using pre-trained deep networks with different images.

🔖

Bu Makaleye Atıf Yap

KUTLU, Y., CAMGÖZLÜ, Y. (2021). Detection of coronavirus disease (COVID-19) from X-ray images using deep convolutional neural networks. Natural and Engineering Sciences, Cilt: 6 - Sayı: 1, 60–74. https://ajindex.com/articles/793157

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (10)

  1. Altan, G., & Kutlu, Y. (2018). Hessenberg Elm autoencoder kernel for deep learning. Journal of Engineering Technology and Applied Sciences, 3(2), 141-151.
  2. Altan, G., Kutlu, Y., & Allahverdi, N. (2019). Deep learning on computerized analysis of chronic obstructive pulmonary disease. IEEE Journal of Biomedical and Health Informatics, 24(5), 1344-1350.
  3. Altan, G., Kutlu, Y., Garbi, Y., Pekmezci, A. Ö., & Nural, S. (2017). Multimedia respiratory database (RespiratoryDatabase@ TR): Auscultation sounds and chest X-rays. Natural and Engineering Sciences, 2(3), 59-72.
  4. Apostolopoulos, I. D., & Mpesiana, T. A. (2020). 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.
  5. Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). 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, 103795. Badnjevic, A., Gurbeta, L., & Custovic, E. (2018). An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings. Scientific reports, 8(1), 1-9.
  6. Camgözlü, Y., & Kutlu, Y. (2020). Analysis of Filter Size Effect In Deep Learning. arXiv preprint arXiv:2101.01115. Chollet, F. (2018). Deep learning with Python (Vol. 361). New York: Manning. Chowdhury, M. E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B.,. & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, 132665-132676.
  7. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis,. New York: Wiley. Edgar Lorente, (2020). COVID-19 pneumonia-evolution over a week https://radiopaedia.org/cases/COVID-19-pneumonia-evolution-over-a-week-1?lang=us, version (2020).
  8. Falk, T., Mai, D., Bensch, R., Çiçek, Ö., Abdulkadir, A., Marrakchi, Y.,. & Ronneberger, O. (2019). U-Net: deep learning for cell counting, detection, and morphometry. Nature Methods, 16(1), 67-70.
  9. Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1-13.
  10. Ghoshal, B., & Tucker, A. (2020). Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. ArXiv preprint arXiv:2