Classification of multi-label electrocardiograms utilizing the efficientNet CNN model

Basit Öğe Kaydı
dc.authorid0000-0001-9219-2262en_US
dc.authorid0000-0003-2995-8282en_US
dc.contributor.authorAkkuzu, Nida
dc.contributor.authorUçan, Murat
dc.contributor.authorKaya, Mehmet
dc.date.accessioned2024-09-12T06:14:08Z
dc.date.available2024-09-12T06:14:08Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractUsing electrocardiogram (ECG) signal images, the status of Covid-19, Abnormal heartbeat, Myocardial infarction, Myocardial Infarction History and Normal findings can be detected. Disease detections made with traditional methods by specialist doctors in the field can lead to mistreatment due to human error. The successes obtained from the classification studies using ECG images in the literature do not have an acceptable success rate yet. The aim of this study is to propose a new approach with high success rate for the detection of diseases using ECG images and to analyze detailed test results. A publicly available dataset containing 5-class ECG images was used in this study. Training and testing processes were carried out using the EfficientNetB0 convolutional neural network architecture. Afterwards, the results were analyzed in detail, graphs were drawn and the results were compared with other studies in the literature. The proposed multi-class classification architecture offers 99.13% accuracy. With the success achieved, it was superior to other studies in the literature. This study will contribute to the rapid and reliable detection of 5 different findings that can be detected from ECG images and to more accurate treatment of patients.en_US
dc.identifier.citationAkkuzu, N., Uçan, M. ve Kaya, M. (2023). Classification of multi-label electrocardiograms utilizing the efficientNet CNN model. 2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023 içinde, 268-272.en_US
dc.identifier.endpage272en_US
dc.identifier.isbn979-835036978-6
dc.identifier.scopus2-s2.0-85202431715
dc.identifier.scopusqualityN/A
dc.identifier.startpage268en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/10629383
dc.identifier.urihttps://hdl.handle.net/11468/28839
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Murat
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023
dc.relation.isversionof10.1109/ICDABI60145.2023.10629383en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDeep learningen_US
dc.subjectECGen_US
dc.subjectEfficientNeten_US
dc.subjectElectrocardiogramen_US
dc.titleClassification of multi-label electrocardiograms utilizing the efficientNet CNN modelen_US
dc.titleClassification of multi-label electrocardiograms utilizing the efficientNet CNN model
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Classification of Multi-Label Electrocardiograms Utilizing the EfficientNet CNN Model.pdf
Boyut:
1.32 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Konferans Öğesi
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Diyarbakır Teknik Bilimler Meslek Yüksekokulu Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu