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Pneumonia detection and classification using neural network

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorAjwad, Rasif
dc.contributor.authorShowkat, Jobayer Bin
dc.contributor.authorAlavi, Salsabil Hossain
dc.contributor.authorSadman, Sean
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-10-05T10:07:39Z
dc.date.available2025-10-05T10:07:39Z
dc.date.copyright2020
dc.date.issued2020-10
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 22-23).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.en_US
dc.description.abstractMedical image classification is of vital importance when it comes to clinical treatment. Numerous imaging techniques exist in diagnosis of various diseases, but X-rays remain one of the most popular techniques. Given that, X-rays are inexpensive and the easiest to perform, it is one of the most frequently used radiology examinations for diagnosis. X-rays are performed in different parts of the body. In this paper, we will focus on chest X-rays which give us images of the lungs, heart, airways and the bones which are present in the chest and spine. Chest X-rays can also display fluid inside the lungs or the area surrounding the lungs. The image produced by chest X-rays can help doctors determine many lung-diseases. However, examining chest X-rays clinically can be tedious and complex. Therefore, computer aided detection can help to achieve more accurate and simpler ways of acquiring correct diagnosis. Computer aided detection techniques include the use of machine learning algorithms and deep learning methods.en_US
dc.description.degreeBachelor of Science in Computer Science
dc.description.statementofresponsibilityJobayer Bin Showkat
dc.description.statementofresponsibilitySalsabil Hossain Alavi
dc.description.statementofresponsibilitySean Sadman
dc.format.extent32 pages
dc.identifier.otherID 17301217
dc.identifier.otherID 17141010
dc.identifier.otherID 17201071
dc.identifier.urihttp://hdl.handle.net/10361/26819
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC 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.
dc.subjectMedical imagesen_US
dc.subjectImage processingen_US
dc.subjectDisease detectionen_US
dc.subjectThoracic diseasesen_US
dc.subjectDeep learningen_US
dc.subjectNeural networksen_US
dc.subjectComputer aided detectionen_US
dc.subject.lcshDiagnostic imaging--Data processing.
dc.subject.lcshNeural networks (Computer science).
dc.subject.lcshPneumonia--Diagnosis.
dc.titlePneumonia detection and classification using neural networken_US
dc.typeThesisen_US

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