A new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM

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dc.authoridKAYA, Mahmut/0000-0002-7846-1769
dc.authoridKUNCAN, Fatma/0000-0003-0712-6426
dc.authoridYiner, Zuleyha/0000-0001-7017-6114
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorYiner, Zuleyha
dc.contributor.authorKaya, Mahmut
dc.contributor.authorKuncan, Fatma
dc.date.accessioned2024-12-24T19:28:28Z
dc.date.available2024-12-24T19:28:28Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractDeclared a pandemic disease, COVID-19 has affected the lives of millions of people and had significant effects on public health. Despite the development of effective vaccines against COVID-19, cases continue to increase worldwide. According to studies in the literature, artificial intelligence methods are used effectively for the detection of COVID-19. In particular, deep-learning-based approaches have achieved very good results in clinical diagnostic studies and other fields. In this study, a new approach using x-ray images is proposed to detect COVID-19. In the proposed method, the angle transform (AT) method is first applied to the x-ray images. The AT method proposed in this study is an important novelty in the literature, as there is no such approach in previous studies. This transformation uses the angle information created by each pixel on the image with the surrounding pixels. Using the AT approach, eight different images are obtained for each image in the dataset. These images are trained with a hybrid deep learning model, which combines GoogleNet and long short-term memory (LSTM) models, and COVID-19 disease detection is carried out. A dataset from the Mendeley database is used to test the proposed approach. A high classification accuracy of 98.97% is achieved with the AT + GoogleNet + LSTM approach. The results obtained were also compared with other studies in the literature. The presented results reveal that the proposed method is successful for COVID-19 detection using chest x-ray images. Direct transfer methods were also applied to the data set used in the study. However, worse results were observed according to the proposed approach. The proposed approach has the flexibility to be applied effectively to different medical images.
dc.identifier.doi10.1088/1361-6501/ac8ca4
dc.identifier.issn0957-0233
dc.identifier.issn1361-6501
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85140853396
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1088/1361-6501/ac8ca4
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7069
dc.identifier.volume33
dc.identifier.wosWOS:000869978500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIop Publishing Ltd
dc.relation.ispartofMeasurement Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectCOVID-19
dc.subjectangle transformation
dc.subjectGoogleNet
dc.subjectLSTM
dc.titleA new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM
dc.typeArticle

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