Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images

Autor: Durmuş Özdemir, Naciye Nur Arslan
Jazyk: English<br />Turkish
Rok vydání: 2022
Předmět:
Zdroj: Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol 10, Iss 2, Pp 628-640 (2022)
Druh dokumentu: article
ISSN: 2148-2446
DOI: 10.29130/dubited.976118
Popis: 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.
Databáze: Directory of Open Access Journals