Alam, Md. AshrafulReza, Md. TanzimKabir, Asif RezwanRoy, ShutirthaZerin, NusratAfrin, Sheikh ShariaChoudhury, Anika Jahan2022-06-012022-06-0120222022-01ID 18301230ID 18301028ID 18101533ID 18101528ID 18301016http://hdl.handle.net/10361/16785Cataloged from PDF version of thesis.Includes bibliographical references (pages 34-35).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.The beginning of 2020 will always be a dreadful chapter in human history. Even with all the recent advancements in the medical sector, the COVID-19 virus proved to be a major challenge for doctors all over the world. The virus affected different people in different ways. One of its deadliest symptoms can be observed in our lungs. COVID-19 can cause various complications in the lungs such as pneumonia, acute respiratory distress syndrome (ARDS), sepsis, etc. This pandemic, being highly contagious, can spread and affect a large number of the population in a very short period. This results in many patients not receiving proper treatment at the appropriate time. Our proposed CNN model will be able to automate the entire detection and classification process. It will be trained using large amounts of Xray images of lungs, which will provide it with the necessary feature knowledge to distinguish between an infected lung and a healthy one.35 pagesenBrac 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.Covid-19CNNSupervised learningX-ray imageTensorflowNeural networks (Computer science)Thesis