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Comparison of deep learning architectures for CXR opacity detection

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, adjud...

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Bibliographic Details
Main Authors: Rafiqin Roslan, Siti Nurhajar, Che Azemin, Mohd Zulfaezal, Md. Ali, Mohd. Adli, Mohd Tamrin, Mohd Izzuddin, Jamaludin, Iqbal
Format: Conference or Workshop Item
Language:English
English
Published: Association for Computing Machinery (ACM) 2022
Subjects:
RC731 Specialties of Internal Medicine-Diseases of The Respiratory System
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/98251/7/98251_Comparison%20of%20Deep%20Learning%20Architectures%20for%20CXR%20Opacity%20Detection.pdf
http://irep.iium.edu.my/98251/8/98251_Screenshot%20of%20the%20Proceedings.pdf
http://irep.iium.edu.my/98251/
https://dl.acm.org/doi/10.1145/3524304.3524316
https://doi.org/10.1145/3524304.3524316
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http://irep.iium.edu.my/98251/7/98251_Comparison%20of%20Deep%20Learning%20Architectures%20for%20CXR%20Opacity%20Detection.pdf
http://irep.iium.edu.my/98251/8/98251_Screenshot%20of%20the%20Proceedings.pdf
http://irep.iium.edu.my/98251/
https://dl.acm.org/doi/10.1145/3524304.3524316
https://doi.org/10.1145/3524304.3524316

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