CNN Based Covid-19 Detection from Image Processing

Mohammed Ashikur Rahman; Mohammad Rabiul  Islam; Md. Anzir Hossain Rafath; Simron  Mhejabin

doi:10.5614/itbj.ict.res.appl.2023.17.1.7

Authors

Mohammed Ashikur Rahman Department of Computer Science, University of Liberal Arts Bangladesh, 688, Beribadh Road, Mohammadpur, Dhaka 1207, Bangladesh
Mohammad Rabiul Islam Department of Computer Science, American International University Bangladesh, 408 1, Kuratoli, Khilkhet, Dhaka 1229, Bangladesh
Md. Anzir Hossain Rafath Department of Computer Science and Engineering, University of South Asia, Amin Bazar, Savar, Dhaka 1348, Bangladesh
Simron Mhejabin Department of Computer Science, University of Liberal Arts Bangladesh, 688, Beribadh Road, Mohammadpur, Dhaka 1207, Bangladesh

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.7

Keywords:

Covid-19 detection, CNN, DenseNet, image processing, pneumonia detection

Abstract

Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.

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