Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks
Yükleniyor...
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
COVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.
Açıklama
WOS:000618279300001
PMID: 33821092
PMID: 33821092
Anahtar Kelimeler
Classification, Computer-aided diagnosis, Convolutional neural networks, coronavirus, COVID19, Deep learning, Radiology
Kaynak
International Journal of Imaging Systems and Technology
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
31
Sayı
2
Künye
Polat, H., Özerdem, M.S., Ekici, F. ve Akpolat, V. (2021). Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks. International Journal of Imaging Systems and Technology, 31(2), 509-524.
