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dc.contributor.advisorAlam, Md. Golam Rabiul
dc.contributor.advisorReza, Md Tanzim
dc.contributor.authorChowdhury, Asif Hasan
dc.contributor.authorIslam, Md. Fahim
dc.contributor.authorRiad, M Ragib Anjum
dc.contributor.authorHashem, Faiyaz Bin
dc.date.accessioned2023-10-16T06:43:28Z
dc.date.available2023-10-16T06:43:28Z
dc.date.copyright©2022
dc.date.issued2022-09-28
dc.identifier.otherID 18101278
dc.identifier.otherID 18101501
dc.identifier.otherID 18101472
dc.identifier.otherID 18101278
dc.identifier.urihttp://hdl.handle.net/10361/21844
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 38-39).
dc.description.abstractThe significant advancements in computational power create the vast opportunity for using Artificial Intelligence in different applications of healthcare and medical science. A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging a combination of SWIN transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available technology that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model Swin Transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this thesis, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information.en_US
dc.format.extent51 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.subjectAIen_US
dc.subjectVGG19en_US
dc.subjectInception V3en_US
dc.subjectDenseNet201en_US
dc.subjectSWIN transformeren_US
dc.subjectFeder- ated learningen_US
dc.subject.lcshBiomedical engineering
dc.subject.lcshNeural networks (Computer science)
dc.titleA hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNNen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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