Title: An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images

Authors: Aakanksha Sharaff; Madhur Singhal; Arham Chouradiya; Pavan Gupta

Addresses: Department of Computer Science and Engineering, National Institute of Technology Raipur, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, India

Abstract: COVID-19 is a pandemic and a highly contagious disease that can severely damage the respiratory organs. Tuberculosis is also one of the leading respiratory diseases that affect public health. While COVID-19 has pushed the world into chaos and tuberculosis is still a persistent problem in many countries. A chest X-ray can provide plethora of information regarding the type of disease and the extent of damage to the lungs. Since X-rays are widely accessible and can be used in the diagnosis of COVID-19 or tuberculosis, this study aims to leverage those property to classify them in the category of COVID-19 infected lungs, tuberculosis infected lungs or normal lungs. In this paper, an ensemble deep learning model consisting of pre-trained models for feature extraction is used along with machine learning classifiers to classify the X-ray images. Various ensemble models were implemented and highest achieved accuracy among them was observed as 93%.

Keywords: ensemble learning; COVID-19; tuberculosis; machine learning; MobileNet; Xception; ResNet50.

DOI: 10.1504/IJBM.2023.130653

International Journal of Biometrics, 2023 Vol.15 No.3/4, pp.459 - 479

Received: 20 Jul 2021
Accepted: 25 Mar 2022
Published online: 02 May 2023 *

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