Classification of multi-label electrocardiograms utilizing the efficientNet CNN model
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Using 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.
Açıklama
Anahtar Kelimeler
Classification, Convolutional Neural Network, Deep learning, ECG, EfficientNet, Electrocardiogram
Kaynak
2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Akkuzu, 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.
