An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images Online publication date: Tue, 02-May-2023
by Aakanksha Sharaff; Madhur Singhal; Arham Chouradiya; Pavan Gupta
International Journal of Biometrics (IJBM), Vol. 15, No. 3/4, 2023
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%.
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