Comparison of deep learning architectures for CXR opacity detection

• 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

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...