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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.authorSagor, Mostofa Kamal
dc.contributor.authorJahan, Ishrat
dc.contributor.authorChowdhury, Susmita
dc.contributor.authorAnsary, Rubayet
dc.date.accessioned2021-12-26T04:37:41Z
dc.date.available2021-12-26T04:37:41Z
dc.date.copyright2021
dc.date.issued2021-01
dc.identifier.otherID 17301106
dc.identifier.otherID 17101458
dc.identifier.otherID 17101025
dc.identifier.otherID 20241050
dc.identifier.urihttp://hdl.handle.net/10361/15753
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 29-31).
dc.description.abstractAmong the most convenient bacteriological assessments for the diagnosis and treatment with several health complications is the chest X-Ray. The World Health Organization (WHO) estimates, for instance, that pneumonic plague induces between 250,000 to 500,000 fatalities annually. Pneumonia and flu are serious challenges towards global health as well as being a source of significant death rates globally. [1]. In X-Ray imaging, it is a common technique to standardize the extracted image reconstruction with usual uniform disciplines taken before the study. Unfortunately, there has been relatively little study on several separate lung disease monitoring, including X-Ray picture analysis and poorly labelled repositories. Our paper suggests an effective approach for the detection of lung disease trained on automated chest X-ray images that could encourage radiologists in their moral choice. Besides, with a weighted binary classifier, a particular technique is also deployed that will optimally leverage the weighted predictions from optimal deep neural networks such as InceptionV3, VGG16 and ResNet50. In addition to the existing, transfer learning, along with more rigorous academic training and testing sets, is used to fine-tune deep neural networks to achieve higher internal processes. In comparison, 88.14 percent test accuracy was obtained with the final proposed weighted binary classifier, where other models give us about 76.91 percent average accuracy. For a brief recurring diagnosis, the legally prescribed procedure may also be used which may increase the course of the same condition for physicians. For a prompt diagnosis of pneumonia, the suggested approach should be used and can improve the diagnosis process for health practitioners.en_US
dc.description.statementofresponsibilityMostofa Kamal Sagor
dc.description.statementofresponsibilityIshrat Jahan
dc.description.statementofresponsibilitySusmita Chowdhury
dc.description.statementofresponsibilityRubayet Ansary
dc.format.extent31 Pages
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.subjectLung diseaseen_US
dc.subjectChest X-ray imagesen_US
dc.subjectConvolution neural network (CNN)en_US
dc.subjectDeep learningen_US
dc.subjectTransfer learningen_US
dc.subjectDiagnostics facilitated by electronicsen_US
dc.subject.lcshDeep learning
dc.titleAn efficient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modelingen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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