Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Furkan Eryılmaz; Hacer Karacan

doi:10.29130/dubited.1011829

TR EN

Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Abstract

Solunum sistemine etki eden ve ileri vakalarda ölüme neden olan korona virüs salgını yaklaşık iki yıldır devam etmektedir. Her ülkenin salgın ile mücadele yöntemi farklı olmasına rağmen ortak izlenen metot ise hastalığın tespiti ve izolasyonudur. Tespit ve izolasyon için en kritik adım ise COVID-19 tanısının doğru ve hızlı konulmasıdır. Akciğer X-Ray görüntülerinde virüse özgü bulgulara rastlanılması, bu verilerin hastalık teşhisinde kullanılabileceğini göstermektedir. İlgili çalışmanın amacı, makine öğrenmesi yöntemleri ile COVID-19 ve diğer akciğer hastalıklarına ait X-Ray görüntülerini işleyerek çoklu sınıflandırma yapmaktır. Bu sayede kriz anında tanı koyma ve izolasyon için yardım alınacak alanında uzman olmayan personele mobil cihazlar vasıtasıyla karar aşamasında destek sağlanması hedeflenmektedir. Bu amaçla: COVID-19, Normal, Akciğer Opasitesi, Diğer Pnömoni etiketlerine ait 11,293 X-Ray görüntüsünden oluşan veri seti MobileNetV2, NASNetMobile, Xception ve DenseNet121 CNN ağları kullanılarak sınıflandırılmış ve sonuçlar karşılaştırılmıştır. En başarılı sonuçlar DenseNet121 ve MobileNet ağları ile elde edilmiş olup sırası ile %92,16 ve %91,78 doğruluk oranıyla sınıflandırma gerçekleştirilmiştir.

Keywords

Evrişimsel sinir ağları, Koronavirüs, COVID-19, Transfer öğrenimi, X-Ray

Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

Abstract

The corona virus epidemic, which affects the respiratory system and causes death in advanced cases, has been going on for about two years Although each country's method of fighting the epidemic is different, the common and valid method is the detection and isolation of the disease. The most critical step for detection and isolation is the correct and fast diagnosis of COVID-19. Virus-specific findings in lung X-ray images shows that these data can be used in the diagnosis of the disease. The aim of the related study is to multi classify by processing X-Ray images of COVID-19 and other lung diseases with machine learning methods. In this way, it is aimed to provide support to the personnel who are not experts in their fields, who will be helped for diagnosis and isolation during the crisis, at the decision stage through mobile devices. For this purpose: The data set consisting of 11,293 X-Ray images of COVID-19, Normal, Lung Opacity, Other Pneumonia labels was classified using the MobileNetV2, NASNetMobile, Xception and DenseNet121 CNN networks and the results were compared. The most successful results were obtained with DenseNet121 and MobileNet networks, and classification was performed with 92.16% and 91.78% accuracy rates, respectively.

Keywords

Convolutional neural network, Coronavirus, Transfer learning, X-Ray

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Details

Primary Language

Turkish

Subjects

Engineering

Journal Section

Research Article

Authors

Furkan Eryılmaz ^*
0000-0003-1389-6478
Türkiye

Hacer Karacan
0000-0001-6788-008X
Türkiye

Publication Date

December 31, 2021

Submission Date

October 19, 2021

Acceptance Date

November 21, 2021

Published in Issue

Year 2021 Volume: 9 Number: 6

DOI

https://doi.org/10.29130/dubited.1011829

IZ

https://izlik.org/JA34KT59YE

APA

Eryılmaz, F., & Karacan, H. (2021). Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması. Duzce University Journal of Science and Technology, 9(6), 26-39. https://doi.org/10.29130/dubited.1011829

AMA

1.Eryılmaz F, Karacan H. Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması. DUBİTED. 2021;9(6):26-39. doi:10.29130/dubited.1011829

Chicago

Eryılmaz, Furkan, and Hacer Karacan. 2021. “Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif Ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması”. Duzce University Journal of Science and Technology 9 (6): 26-39. https://doi.org/10.29130/dubited.1011829.

EndNote

Eryılmaz F, Karacan H (December 1, 2021) Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması. Duzce University Journal of Science and Technology 9 6 26–39.

IEEE

[1]F. Eryılmaz and H. Karacan, “Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması”, DUBİTED, vol. 9, no. 6, pp. 26–39, Dec. 2021, doi: 10.29130/dubited.1011829.

ISNAD

Eryılmaz, Furkan - Karacan, Hacer. “Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif Ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması”. Duzce University Journal of Science and Technology 9/6 (December 1, 2021): 26-39. https://doi.org/10.29130/dubited.1011829.

JAMA

1.Eryılmaz F, Karacan H. Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması. DUBİTED. 2021;9:26–39.

MLA

Eryılmaz, Furkan, and Hacer Karacan. “Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif Ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması”. Duzce University Journal of Science and Technology, vol. 9, no. 6, Dec. 2021, pp. 26-39, doi:10.29130/dubited.1011829.

Vancouver

1.Furkan Eryılmaz, Hacer Karacan. Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması. DUBİTED. 2021 Dec. 1;9(6):26-39. doi:10.29130/dubited.1011829

Integration of mobile deep networks and machine learning methods for flood risk classification: 2D grayscale transformation of data, feature intersection

Acta Geophysica

https://doi.org/10.1007/s11600-025-01624-3

Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Abstract

Keywords

Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

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Integration of mobile deep networks and machine learning methods for flood risk classification: 2D grayscale transformation of data, feature intersection