CNN Based Covid-19 Detection from Image Processing

被引:0
|
作者
Rahman, Mohammed Ashikur [1 ]
Islam, Mohammad Rabiul [2 ]
Rafath, Md. Anzir Hossain [3 ]
Mhejabin, Simron [1 ]
机构
[1] Univ Liberal Arts Bangladesh, Dept Comp Sci, 688 Beribadh Rd, Dhaka 1207, Bangladesh
[2] Amer Int Univ Bangladesh, Dept Comp Sci, 408-1 Kuratoli, Dhaka 1229, Bangladesh
[3] Univ South Asia, Dept Comp Sci & Engn, Dhaka 1348, Bangladesh
关键词
Covid-19; detection; CNN; DenseNet; image processing; pneumonia detection;
D O I
10.5614/itbj.ict.res.appl.2023.17.1.7
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Covid-19 is a respirational condition that looks much like pneumonia. 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.
引用
收藏
页码:99 / 113
页数:15
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