KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya University Journal of Computer and Information Sciences / 2022 / Cilt: 5 Sayı: 2

Application with deep learning models for COVID-19 diagnosis

Fuat TÜRK
Yunus KÖKVER
Sayfa
169–180
Yayın Tarihi
2022-08-31
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)

The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world.
📊
Temel Bulgular & Veri:Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19.
💡
Sonuç & Katkı:The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.
🔬Tür:Araştırma Makalesi
📍Kapsam:2019 Verileri
⚙️Değişkenler:COVID-19 diagnosis · DenseNet · NasNet-Mobile

Abstract

COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

🔖

Bu Makaleye Atıf Yap

TÜRK, F., KÖKVER, Y. (2022). Application with deep learning models for COVID-19 diagnosis. Sakarya University Journal of Computer and Information Sciences, Cilt: 5 Sayı: 2, 169–180. https://ajindex.com/articles/946631

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (11)

  1. U.G. Kraemer Moritz et al., “Data curation during a pandemic and lessons learned from COVID-19.” Nat. Comput. Sci, vol. 1 (1), pp. 9–10, 2021
  2. H. Panwar, P.K. Gupta, S. M. Khubeb, R.M. Menendez, P. Bhardwaj, V. Singh, “A Deep Learning and Grad-CAM based Color Visualization Approach for Fast Detection of COVID-19 Cases using Chest X-ray and CT-Scan Images,” Chaos, Solitons Fractals, vol. 140, 2020.
  3. P. Rai, B. K. Kumar, V. K. Deekshit, I. Karunasagar, “Detection technologies and recent developments in the diagnosis of COVID-19 infection.”, Appl. Microbiol. Biotechnol, pp. 1–15,2021. https://doi.org/10.1007/S00253- 020-11061 [4] C. C. Nathaniel et al., “Multiplexed detection and quantification of human antibody response to COVID-19 infection using a plasmon enhanced biosensor platform”, Biosens. Bioelectron, 171, pp. 112679-112679, 2021.
  4. L. Fang, X. Wang. "Mathematical modelling of two-axis photovoltaic system with improved efficiency." Elektronika Ir Elektrotechnika, vol. 21. 4, pp 40-43, 2015
  5. V. Manivel, A. Lesnewski, S. Shamim, G. carbonatto, T. Govindan, “CLUE: COVID-19 lung ultrasound in emergency department”, Emerg. Med. Australasia (EMA), vol. 32 (4), pp. 694–696, 2020
  6. S. Yang, Y. Zhang, J. Shen, “Clinical potential of UTE-MRI for assessing COVID -19: patient- and lesion-based comparative analysis”, Magn. Reson. Imag., vol. 52 (2), pp. 397–406, 2020
  7. A. Narin, C. Kaya, Z. Pamuk, “Automatic Detection of Coronavirus Disease (Covid19) Using X-Ray Images and Deep Convolutional Neural Networks,” arXiv preprint arXiv, pp.10849, 2020.
  8. L. Luo, Z. Luo, Y. Jia, C. Zhou, J. He, J. Lyu, X. Shen, “CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method”, BMC Pulm. Med., vol. 20 (11), pp. 719–739, 2020
  9. G. Jia,H.Keung, L.Y.Xu, “Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method”, vol. 134, 2021.
  10. P. Singh, M. Vallejo, I.M. El-Badawy, A. Aysha, J. Madhanagopal, A. Athif, M. Faudzi. “Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms”, Computers in Biology and Medicine, vol. 136, 2021
  11. M. Gour, S. Jain. “Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification”, Computers in Biology an