COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer

dc.authorid0000-0002-8611-701Xen_US
dc.authorid0000-0002-0381-9631en_US
dc.contributor.authorSert,Eser
dc.contributor.authorKızıloluk,Soner
dc.date.accessioned2022-12-19T12:40:18Z
dc.date.available2022-12-19T12:40:18Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractCoronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription-polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people's lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch.en_US
dc.identifier.doi10.1007/s11517-022-02553-9.Epub 2022 Apr 8
dc.identifier.endpage1612en_US
dc.identifier.issue60en_US
dc.identifier.pmid35396625
dc.identifier.scopus2-s2.0-85127644543en_US
dc.identifier.startpage1595en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1194
dc.identifier.volume6en_US
dc.identifier.wosWOS:000781187800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorSert, Eser
dc.institutionauthorKızıloluk, Soner
dc.language.isoenen_US
dc.publisherMalatya Turgut Özal Üniversitesien_US
dc.relation.ispartofNational Library of Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectColon Cancer Diagnosisen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectGradient-Based Optimizer (GBO)en_US
dc.subjectHyperparameter Optimizationen_US
dc.titleCOVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizeren_US
dc.typeArticleen_US

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