Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images
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Dosyalar
Tarih
2022
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study aimed to present an analysis of deep transfer learning models to support the early diagnosis of Covid- 19 disease using X-ray images. For this purpose, the deep transfer learning models VGG-16, VGG-19, Inception V3 and Xception, which were successful in the ImageNet competition, were used to detect Covid-19 disease. Also, 280 chest x-ray images were used for the training data, and 140 chest x-ray images were used for the test data. As a result of the statistical analysis, the most successful model was Inception V3 (%92), the next successful model was Xception (%91), and the VGG-16 and VGG-19 models gave the same result (%88). The proposed deep learning model offers significant advantages in diagnosing covid-19 disease issues such as test costs, test accuracy rate, staff workload, and waiting time for test results.
Açıklama
Anahtar Kelimeler
Biyomedikal bilişim, Derin öğrenme, Covid-19 teşhisi, Görüntü sınıflandırma
Kaynak
Düzce Üniversitesi Bilim ve Teknoloji Dergisi
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Scopus Q Değeri
Cilt
10
Sayı
2
