Binary classification of tuberculosis CXR images across diverse range of CNN architectures: a comparative study

This paper investigates the performance of widely used pre-trained CNN architectures (VGG16, MobileNetV3, DenseNet121, and RegNet040) across diverse datasets, particularly focusing on tuberculosis (TB) detection using Chest X-Rays (CXRs). Deep learning (DL) techniques applied to CXRs aid radiologist...

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Bibliographic Details

Main Authors:	Meraj, Syeda Shaizadi, Shah, Asadullah, Ismail, Ahsiah, Tengku Sembok, Tengku Mohd, Shadab, Syed, Aftab, Syed
Format:	Proceeding Paper
Language:	en en
Published:	IEEE 2025
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/123198/1/123198_Binary%20classification%20of%20tuberculosis.pdf http://irep.iium.edu.my/123198/2/123198_Binary%20classification%20of%20tuberculosis_SCOPUS.pdf http://irep.iium.edu.my/123198/ https://ieeexplore.ieee.org/document/11119808
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