Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning

Gözde Kahraman; Zafer Civelek

doi:10.29137/umagd.1159663

TR EN

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Abstract

Yeni koronavirüs hastalığı (COVID-19) ilk olarak Aralık 2019'un sonlarına doğru Çin'de ortaya çıkarak tüm dünyada bir pandemi haline geldi. Hastalık çok sayıda insanın enfekte olmasına ve ölmesine neden oldu. Hastalığın hızlı teşhis edilmesi bulaşmanın control edilmesinde büyük önem taşımaktadır. Bilgisayarlı Tomografi cihazı, COVID-19 hastalığının teşhisinde başarılı sonuçlar elde etmektedir. Bu çalışmada, özel bir hastanenin Mart 2020 – Kasım 2020 tarihleri arasında teşhis edilmiş 7200 akciğer Bilgisayarlı Tomografi görüntüsü uzman hekimler yardımıyla iki sınıfla ayrılarak (COVID-19 ve normal) veri setleri oluşturulmuştur. Yapay Sinir Ağı (YSA), Destek Vektör Makinesi (SVM), Makine Öğrenmesi algoritmalarından K-En Yakın Komşuluk (KNN) algoritmaları ve ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception Derin Öğrenme modelleri üzerinde doğrulama ve test işlemleri gerçekleştirilmiştir. Gerçekleştirilen çalışmalar sonucunda DenseNet-201 mimarisi, sırasıyla %99,35 eğitim ve test %98,75 doğruluk oranları ile derin öğrenme modellerinden en yüksek sonucu almıştır. Makine öğrenmesinden ANN %97,6, KNN %97,4 ve SVM %96,9 doğruluk oranları elde edilmiştir.

Keywords

derin öğrenme, makine öğrenmesi, cnn

Thanks

Çankırı Özel Karatekin Hastanesi Müdürü Sayın Önder İZMİRLİOĞLU’na, Radyoloji Bölümü Uzman Doktoru Sayın Recep ER’e, Radyoloji Teknikeri Sayın Erkan KULOĞLU’na ve tüm Radyoloji bölümü çalışanlarına teşekkürlerimizi ve saygılarımızı sunarız.

Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning

Abstract

Abstract The new virus disease (COVID-19) first came to China towards the end of December 2019 and became a pandemic all over the world. The disease caused a large number of people to be infected and die. Rapid diagnosis of the disease is of great importance in controlling transmission. A computed Tomography device provides successful results in the diagnosis of COVID-19 disease. In this study, two-class (COVID-19 and normal) data sets were created from 7200 lung Computed Tomography images diagnosed between March 2020 and November 2020 in a private hospital with the help of specialist physicians. Verification and testing processes were carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) algorithms from Machine Learning algorithms, and ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception architectures from Deep Learning models. As a result of the studies, the DenseNet-201 architecture obtained the highest result from deep learning models with %99,35 training and test %98,75 accuracy rates, respectively. ANN %97,6, KNN %97,4 and SVM %96,9 accuracy rates were obtained from machine learning.

Keywords

machine learning, deep learning, cnn

References

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Details

Primary Language

English

Subjects

Electrical Engineering

Journal Section

Research Article

Authors

Gözde Kahraman ^*
0000-0001-6478-5466
Türkiye

Zafer Civelek
0000-0001-6838-3149
Türkiye

Publication Date

December 31, 2023

Submission Date

August 9, 2022

Acceptance Date

July 21, 2023

Published in Issue

Year 2023 Volume: 15 Number: 3

DOI

https://doi.org/10.29137/umagd.1159663

IZ

https://izlik.org/JA36ED87ZK

APA

Kahraman, G., & Civelek, Z. (2023). Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. International Journal of Engineering Research and Development, 15(3), 49-63. https://doi.org/10.29137/umagd.1159663

AMA

1.Kahraman G, Civelek Z. Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. IJERAD. 2023;15(3):49-63. doi:10.29137/umagd.1159663

Chicago

Kahraman, Gözde, and Zafer Civelek. 2023. “Diagnosing Covid-19 Disease from Computed Tomography Images With Deep Learning and Machine Learning”. International Journal of Engineering Research and Development 15 (3): 49-63. https://doi.org/10.29137/umagd.1159663.

EndNote

Kahraman G, Civelek Z (December 1, 2023) Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. International Journal of Engineering Research and Development 15 3 49–63.

IEEE

[1]G. Kahraman and Z. Civelek, “Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning”, IJERAD, vol. 15, no. 3, pp. 49–63, Dec. 2023, doi: 10.29137/umagd.1159663.

ISNAD

Kahraman, Gözde - Civelek, Zafer. “Diagnosing Covid-19 Disease from Computed Tomography Images With Deep Learning and Machine Learning”. International Journal of Engineering Research and Development 15/3 (December 1, 2023): 49-63. https://doi.org/10.29137/umagd.1159663.

JAMA

1.Kahraman G, Civelek Z. Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. IJERAD. 2023;15:49–63.

MLA

Kahraman, Gözde, and Zafer Civelek. “Diagnosing Covid-19 Disease from Computed Tomography Images With Deep Learning and Machine Learning”. International Journal of Engineering Research and Development, vol. 15, no. 3, Dec. 2023, pp. 49-63, doi:10.29137/umagd.1159663.

Vancouver

1.Gözde Kahraman, Zafer Civelek. Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning. IJERAD. 2023 Dec. 1;15(3):49-63. doi:10.29137/umagd.1159663

Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Abstract

Keywords

Thanks

Diagnosing Covid-19 Disease from Computed Tomography Images with Deep Learning and Machine Learning

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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