Comparison of deep learning architectures for CXR opacity detection

Rafiqin Roslan, Siti Nurhajar and Che Azemin, Mohd Zulfaezal and Md. Ali, Mohd. Adli and Mohd Tamrin, Mohd Izzuddin and Jamaludin, Iqbal (2022) Comparison of deep learning architectures for CXR opacity detection. In: 11th International Conference on Software and Computer Applications (ICSCA 2022),, Melaka, Malaysia.

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Abstract

Previous research has shown that x-ray images can be labeled based on their abnormalities. The problem with the labels includes inconsistencies in the assignment of the abnormality which may lead to overestimation of the model performance. To overcome the problem of the majority-vote approach, adjudicated labels could be used. This researchwork highlights the comparison of deep learning architectures for chest x-ray opacity detection. This study aims to investigate the best performance of the different deep learning models used when they are trained with the publicly available deep learning architectures and data set rather than using one type of deep learning model. Among the different deep learning architectures models used, an optimal model would be identified based on the best performance metrics.

Item Type:	Conference or Workshop Item (Invited Papers)
Uncontrolled Keywords:	Machine Learning, Deep Learning, Convolutional Neural Network
Subjects:	R Medicine > RC Internal medicine > RC731 Specialties of Internal Medicine-Diseases of The Respiratory System T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Allied Health Sciences Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Science Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science
Depositing User:	Mohd Izzuddin Mohd Tamrin
Date Deposited:	10 Jun 2022 09:45
Last Modified:	06 Jul 2022 15:49
URI:	http://irep.iium.edu.my/id/eprint/98251

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