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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.advisorReza, Md Tanzim
dc.contributor.authorTanvir, Nazmul Karim
dc.contributor.authorYeasin, MD. Nadim
dc.contributor.authorMD. Mahmuduzzaman, Sarker
dc.contributor.authorAra, Jannat
dc.date.accessioned2023-10-16T05:10:50Z
dc.date.available2023-10-16T05:10:50Z
dc.date.copyright©2022
dc.date.issued2022-09-28
dc.identifier.otherID 18101054
dc.identifier.otherID 18101560
dc.identifier.otherID 18301073
dc.identifier.otherID 17301065
dc.identifier.urihttp://hdl.handle.net/10361/21837
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 29-31).
dc.description.abstractHeart is the core of human body. A normal heart beats almost 1,15,200 times in a day and 80 beats per second to make us live alive. But we often take it granted and do uncertain thinks which stops it to function perfectly. In today’s world cardiovascular diseases(CVDs) almost kill 17-18 million life’s each year worldwide which makes it the biggest disease of death. If early detection of heart malfunction or Heart failure(HF) can be detect millions of people will able to breath even longer than usual. In our research our main aim is to create an automated Deep Learning based model which will predict HF and the depth of the condition. Moreover, using which type of cardiac MRI image slice we can get better result will be consider to be our main research goal. For this we choose a cardiac MRI dataset which consists of 1100 different heart patients image having different slices in different pattern. Furthermore, with more observation and leveling different parameter with the help of Ejection Fraction(EF) values which depends on systole diastole value of heart we able to predict the heart failure with an efficient result. AI, ML & deep learning is the new trend for solving real life human problems. We used different Convolution Neural Network architecture and obtained accuracy are VGG-16(88.15%), VGG- 19(87.93%), ResNet-50 (75.85%), ResNet-101 (79.53%) Inception-V3 (85.27%). Our model is being used to find the suitable result to detect the Heart Failure(HF) with Ejection Fraction(EF).en_US
dc.description.statementofresponsibilityNazmul Karim Tanvir
dc.description.statementofresponsibilityMD. Nadim Yeasin
dc.description.statementofresponsibilitySarker MD. Mahmuduzzaman
dc.description.statementofresponsibilityJannat Ara
dc.format.extent41 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.subjectCardiovascular Disease (CVDs)en_US
dc.subjectEjection fraction (EF)en_US
dc.subjectCNNen_US
dc.subjectHeart failure (HF)en_US
dc.subjectVgg-19en_US
dc.subjectVgg-16en_US
dc.subjectInception-V3en_US
dc.subjectCardiac MRI dataen_US
dc.subject.lcshHealth informatics
dc.subject.lcshOptical data processing
dc.titleAn efficient deep learning approach to predict heart failure from image data using ejection fractionen_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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