@inproceedings{1a990f43c0c44d50b43434fa87c5f4cb,
title = "The Multimodal Deep Learning for Diagnosing COVID-19 Pneumonia from Chest CT-Scan and X-Ray Images",
abstract = "Due to the COVID-19 Pandemic, doctors need to make medical decisions for their patients based on many examinations (e.g., polymerase chain reaction test, temperature test, CT-Scans, or X-rays). However, transfer learning has been used in several researches and focuses on only a single modality of biomarkers (e.g., CT-Scan or X-Ray) for diagnosing Pneumonia. In recent studies, a single modality has its own classification accuracy and every different biomarker may provide complementary information for detecting COVID-19 Pneumonia. The COVID-19 virus can be detected by CT-Scan and X-Ray imaging of the chest. In this work, we propose to use concatenation of two different transfer learning models using an open-source dataset of 2500 CT-Scan images and 2500 X-ray images for classifying CT-Scan images and X-ray images into two classes: normal and COVID-19 Pneumonia. We have used DenseNet121, MobileNet, Xception, InceptionV3, ResNet50, and VGG16 models for image recognition in our work. As a result, we achieve the best classification accuracy of 99.87% of the concatenation of ResNet50 and VGG16 networks. We also achieved the best classification accuracy of 98.00% when using a single modality of CT-Scan ResNet50 networks and classification accuracy of 98.93% for X-Ray VGG16 networks. Our multimodal fusion method shows a better classification accuracy compared to the method of using a single modality of biomarkers. ",
keywords = "Concatenate, COVID-19, CT-Scan, Multimodal, Pneumonia, Transfer Learning, X-Ray",
author = "Naufal Hilmizen and Alhadi Bustamam and Devvi Sarwinda",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 ; Conference date: 10-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/ISRITI51436.2020.9315478",
language = "English",
series = "2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "26--31",
editor = "Wibowo, {Ferry Wahyu}",
booktitle = "2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020",
address = "United States",
}