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

Meraj, Syeda Shaizadi and Shah, Asadullah and Ismail, Ahsiah and Tengku Sembok, Tengku Mohd and Shadab, Syed and Aftab, Syed (2025) Binary classification of tuberculosis CXR images across diverse range of CNN architectures: a comparative study. In: 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS2024), 25th August 2025, Bahrain.

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Abstract

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 radiologists in promptly and accurately identifying TB, which is especially critical in low-income regions with constrained diagnostic resources. The research reveals that MobileNetV3 consistently demonstrates superior performance compared to other architectures.

Item Type:	Proceeding Paper (Other)
Additional Information:	6566/123198
Uncontrolled Keywords:	Artificial Intelligence (AI), Convolutional Neural Networks (CNNs), Deep Learning (DL), Machine Learning (ML), Tuberculosis (TB), Pre-trained models
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System 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:	Prof Asadullah Shah
Date Deposited:	17 Sep 2025 09:41
Last Modified:	17 Sep 2025 09:42
URI:	http://irep.iium.edu.my/id/eprint/123198

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