Diagnosis of COVID-19 on chest X-ray (CXR) images using CNN with transfer learning and integrated stacking ensemble learning

Low, Wai Sing and Chow, Li Sze and Solihin, Mahmud Iwan and Handayani, Dini Oktarina Dwi (2024) Diagnosis of COVID-19 on chest X-ray (CXR) images using CNN with transfer learning and integrated stacking ensemble learning. In: 4th Innovative Manufacturing, Mechatronics & Materials Forum 2023 (iM3F 2023), 7th - 8th August 2023, Pekan, Pahang, Malaysia.

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Abstract

COVID-19 caused a pandemic outbreak, resulting in many deaths and severe economic damage since 2019. Hence, the diagnosis of COVID-19 has be-come one of the major fields of research. Although RT-PCR has excellent relia-bility and precision, it is time-consuming and laborious. Therefore, the chest X-ray was used as an alternative and reliable diagnostic tool for COVID-19. How-ever, it requires a radiologist to analyze the X-ray images, which is limited by the availability of experts and time. Henceforth, many researchers deployed auto-mated computer-aided diagnosis with deep learning neural networks to speed up the diagnosis of COVID-19 with high accuracy and reproducibility. This study applied six state-of-art convolutional neural networks (DenseNet201, Mo-bileNetV2, ResNet101V2, VGG16, InceptionNetV3, and Xception) with transfer learning. An integrated stacking ensemble method was used to concatenate DenseNet201, MobileNetV2, VGG16, and Xception to produce a robust and ac-curate diagnostic model for COVID-19. The proposed ensembled CNN model in this study produced a test accuracy of 0.9725, sensitivity of 0.9749, and F1-score of 0.9724.

Item Type:	Proceeding Paper (Other)
Additional Information:	10567/114201
Uncontrolled Keywords:	Diagnosis of COVID-19, Integrated Stacking Ensemble, Transfer Learning, Deep Learning.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Dini Handayani
Date Deposited:	03 Sep 2024 14:56
Last Modified:	03 Sep 2024 15:03
URI:	http://irep.iium.edu.my/id/eprint/114201

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