An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images. (7th February 2023)

• Record Type: Journal Article
• Authors: Sharaff, Aakanksha; Singhal, Madhur; Chouradiya, Arham; Gupta, Pavan
• Is Part Of: International journal of biometrics. Volume 15:Number 3/4(2023)
• Page Start: 459
• Page End: 479
• Publication Date: 2023-02-07
• Subjects: ensemble learning -- COVID-19 -- tuberculosis -- machine learning -- MobileNet -- Xception -- ResNet50; Biometric identification -- Periodicals; Biometry -- Periodicals; 570.15195
• Languages: English
• ISSNs: 1755-8301

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%.