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dc.contributor.advisorUddin, Dr. Jia
dc.contributor.authorRahman, Sakib
dc.contributor.authorRahman, Md. Atifur
dc.contributor.authorKhan, Fariha Ashraf
dc.contributor.authorShahjahan, Shabiba Binte
dc.contributor.authorNahar, Khairun
dc.date.accessioned2018-02-18T09:47:46Z
dc.date.available2018-02-18T09:47:46Z
dc.date.copyright2017
dc.date.issued2017-12-24
dc.identifier.otherID 13101279
dc.identifier.otherID 13101273
dc.identifier.otherID 13101262
dc.identifier.otherID 13301021
dc.identifier.otherID 13101175
dc.identifier.urihttp://hdl.handle.net/10361/9503
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 29-31).
dc.description.abstractBlood grouping is the first and foremost essentiality for many of the major medical procedures. Traditional ways of detecting blood group have remained analogue in this era of digitization and are therefore susceptible to human fallibility. So it would be very efficient and arguably a lifesaving approach if the process of detecting blood can be completed successfully in a cost-effective way with the technologies at hand and without the plausibility of man-made error. This proposition is expected to evaluate the Rh factor as well as the group of a sample blood with its computed image. The whole process excludes a major probability of human error while detecting the agglutination from the traditional method and it would get the task done within a fairly insignificant amount of time. The procedure will start by taking a photo of the sample blood slide followed by the application of a number of algorithms such as grayscale, binary and canny edge detection on it. After that, the detected edges will be counted and thus we will decide the agglutination. The method is established upon real-time dataset including 100 blood samples of people of different ages. The experimental result is almost accurate compared to the real time results from the sample dataset. It can, therefore, conclude the procedure with certain numeric values which were determined after real-time data analysis of images from a mobile camera, to make it simpler and more precise.en_US
dc.description.statementofresponsibilitySakib Rahman
dc.description.statementofresponsibilityMd. Atifur Rahman
dc.description.statementofresponsibilityFariha Ashraf Khan
dc.description.statementofresponsibilityShabiba Binte Shahjahan
dc.description.statementofresponsibilityKhairun Nahar
dc.format.extent31 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports 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.subjectBlood groupen_US
dc.subjectImage processing techniqueen_US
dc.titleBlood group detection using image processing techniquesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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