Covid-19 infected lung detection using machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.
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Brac University
2022
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| Online Erişim: | http://hdl.handle.net/10361/17180 |
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10361-171802022-09-08T21:01:39Z Covid-19 infected lung detection using machine learning Islam, Md. Muntaha Afiat, Mashfurah Biswas, Adrita Syffullah, Md Khalid Rishan, Asadur Rahman Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Covid-19 Deep Neural Network VGG Inception V3 ResNet Ensamble modeling VGG16 ResNet50 Machine learning Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-35). In every 100 years, there has been a pandemic all around the world. The globe faced Plague, Cholera, and Spanish Flu in the years 1720, 1820, and 1920, respectively. Coronavirus, commonly known as Covid-19, is currently circulating in 2020. Coronavirus affects the nose, sinuses, upper neck, and lungs, among other parts of the human respiratory system. Coronaviruses come in a variety of types, although the majority of them aren’t harmful. A brand-new coronavirus epidemic occurred in the Chinese city of Wuhan in December 2019. It was first recognized as SARS-CoV-2 by the World Health Organization, then renamed Covid-19, and it spread swiftly over the world by March 2020. The novel COVID-19 has the potential to develop an infection of the respiratory system. In both the upper and lower respiratory tracts, it can affect the sinuses, nose, throat, windpipe, and lungs.COVID19 is a virus that infects humans via respiratory droplets, coming into contact with a positive for COVID19patientCOVID-19 detection is one of the most challenging undertakings in the globe owing to the virus’s fast spread. The number of persons diagnosed with COVID-19 is increasing dramatically, according to data, with over 16 million confirmed cases. For our research, we’re looking for COVID-19 symptoms in patients’ chest X-ray pictures. We began by gathering information from a variety of sources and categorizing it as COVID-19 positive, other lung illnesses, and normal chest X-ray pictures. Second, we used VGG16, InceptionV3, and ResNet50 to classify the data. The accuracy rates for VGG16, ResNet50, and InceptionV3 were 97.82 percent, 98.89 percent, and 97.65 percent, respectively. Then we combined these classifiers into an ensemble model, and COVDet19 V1 attained an overall accuracy of 97.92 percent. Md. Muntaha Islam Mashfurah Afiat Adrita Biswas Md Khalid Syffullah Asadur Rahman Rishan B. Computer Science 2022-09-08T05:27:45Z 2022-09-08T05:27:45Z 2021 2021-01 Thesis ID 18101079 ID 19101521 ID 18101606 ID 16301036 ID 16301059 http://hdl.handle.net/10361/17180 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Covid-19 Deep Neural Network VGG Inception V3 ResNet Ensamble modeling VGG16 ResNet50 Machine learning Neural networks (Computer science) |
| spellingShingle |
Covid-19 Deep Neural Network VGG Inception V3 ResNet Ensamble modeling VGG16 ResNet50 Machine learning Neural networks (Computer science) Islam, Md. Muntaha Afiat, Mashfurah Biswas, Adrita Syffullah, Md Khalid Rishan, Asadur Rahman Covid-19 infected lung detection using machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Islam, Md. Muntaha Afiat, Mashfurah Biswas, Adrita Syffullah, Md Khalid Rishan, Asadur Rahman |
| format |
Thesis |
| author |
Islam, Md. Muntaha Afiat, Mashfurah Biswas, Adrita Syffullah, Md Khalid Rishan, Asadur Rahman |
| author_sort |
Islam, Md. Muntaha |
| title |
Covid-19 infected lung detection using machine learning |
| title_short |
Covid-19 infected lung detection using machine learning |
| title_full |
Covid-19 infected lung detection using machine learning |
| title_fullStr |
Covid-19 infected lung detection using machine learning |
| title_full_unstemmed |
Covid-19 infected lung detection using machine learning |
| title_sort |
covid-19 infected lung detection using machine learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17180 |
| work_keys_str_mv |
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