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Current Result Document : 6 / 270 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ÈäºÎ X-ray¸¦ È°¿ëÇÑ Äڷγª 19 Áø´Ü ±â¹ý: µö ·¯´× ¸ðµ¨ ¼º´ÉÆò°¡
¿µ¹®Á¦¸ñ(English Title) Post-pandemic Covid-19 Classification using Chest X-Ray Images: Evaluation of deep learning approaches
ÀúÀÚ(Author) Sammy Yap Xiang Bang   ¾çÈñ±Ô   Kim-Ngoc Le Thi   Syed M. Raza      ±è¹®¼º   ÃßÇö½Â   Huigyu Yang      Moonseong Kim   Hyunseung Choo  
¿ø¹®¼ö·Ïó(Citation) VOL 23 NO. 02 PP. 0007 ~ 0008 (2022. 10)
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(Korean Abstract)
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(English Abstract)
As countries begin to lift Covid-19 related activity restrictions, diagnostic testing remains extremely important to identify people infected with Covid-19 to help guide treatment and prevent the further spread of the disease. Diagnosis and classification using Chest X-ray image is one of the effective methods. Diagnosis of Covid-19 using X-ray image has benefits in terms of time, cost, and accuracy compared to PCR and LTFs. Therefore, we compare the performance of recently advanced deep learning techniques for the classification of Covid-19 cases from non Covid-19 cases using X-ray image. We find out that VGG19 performs the best with an accuracy of 94.49%.
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