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- W4225113002 abstract "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." @default.
- W4225113002 created "2022-05-01" @default.
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- W4225113002 date "2022-04-30" @default.
- W4225113002 modified "2023-10-06" @default.
- W4225113002 title "Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images" @default.
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- W4225113002 doi "https://doi.org/10.29130/dubited.976118" @default.
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