Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images

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dc.authorid0000-0002-1451-7874en_US
dc.authorid0000-0002-8040-0519en_US
dc.authorid0000-0001-7684-9690en_US
dc.authorscopusid57221252202en_US
dc.authorscopusid16064211400en_US
dc.authorscopusid8539613600en_US
dc.authorwosidIVV-2556-2023en_US
dc.authorwosidAAG-4138-2019en_US
dc.authorwosidAAH-9838-2021en_US
dc.contributor.authorAcar, Erdi
dc.contributor.authorŞahin, Engin
dc.contributor.authorYılmaz, İhsan
dc.date.accessioned2024-12-16T10:18:48Z
dc.date.available2024-12-16T10:18:48Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractCOVID-19 has caused a pandemic crisis that threatens the world in many areas, especially in public health. For the diagnosis of COVID-19, computed tomography has a prognostic role in the early diagnosis of COVID-19 as it provides both rapid and accurate results. This is crucial to assist clinicians in making decisions for rapid isolation and appropriate patient treatment. Therefore, many researchers have shown that the accuracy of COVID-19 patient detection from chest CT images using various deep learning systems is extremely optimistic. Deep learning networks such as convolutional neural networks (CNNs) require substantial training data. One of the biggest problems for researchers is accessing a significant amount of training data. In this work, we combine methods such as segmentation, data augmentation and generative adversarial network (GAN) to increase the effectiveness of deep learning models. We propose a method that generates synthetic chest CT images using the GAN method from a limited number of CT images. We test the performance of experiments (with and without GAN) on internal and external dataset. When the CNN is trained on real images and synthetic images, a slight increase in accuracy and other results are observed in the internal dataset, but between 3 % and 9 % in the external dataset. It is promising according to the performance results that the proposed method will accelerate the detection of COVID-19 and lead to more robust systems.en_US
dc.identifier.citationAcar, E., Şahin, E., & Yılmaz, İ. (2021). Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. Neural Computing and Applications, 33(24), 17589–17609. https://doi.org/10.1007/s00521-021-06344-5en_US
dc.identifier.doi10.1007/s00521-021-06344-5
dc.identifier.endpage17609en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue24en_US
dc.identifier.pmid3434511
dc.identifier.scopus2-s2.0-85111640581
dc.identifier.startpage17589en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06344-5
dc.identifier.urihttps://hdl.handle.net/20.500.12428/6745
dc.identifier.volume33en_US
dc.identifier.wosWOS:000679229400003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAcar, Erdi
dc.institutionauthorŞahin, Engin
dc.institutionauthorYılmaz, İhsan
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputed tomographyen_US
dc.subjectCOVID-19en_US
dc.subjectData augmentationen_US
dc.subjectDeep learningen_US
dc.subjectGenerative adversarial networken_US
dc.subjectLung segmentationen_US
dc.titleImproving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images
dc.typeArticle

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