Experimental and Theoretical Verification: Detection of Exact Ten Disease from Real Blood Sample

Simple item page
dc.contributor.advisorDr. Mahdy Rahman Chowdhury
dc.contributor.authorJesia Quader Yuki
dc.contributor.authorIshan Sen
dc.contributor.authorKazi Rafshan Hasin
dc.contributor.authorRuwaida Bintey Azam Preyota
dc.contributor.id1530756642
dc.contributor.id1521274642
dc.contributor.id1430310042
dc.contributor.id1510030043
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-01
dc.date.accessioned2025-07-01T06:27:27Z
dc.date.available2025-07-01T06:27:27Z
dc.date.issued2019-04-30
dc.description.abstractThis project is implemented using machine learning technique to classify different disease by determining various shapes of red blood cells. Microscopic images of real red blood cells smears are used to train our software. A deep convolutional neural network is used for the implementation. Compared to traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. So we developed a system by using deep convolutional networks in order to find the classification of RBC & cell segmentation which may help us to measure patient’s disease type. We first take microscopic images of diseased RBC and created rbc patch. Than we use deep convolutional neural network (CNN) to realize RBC classification. The proposed model gives an accuracy of 97.64% on the hold out test. Our method can detect 10 blood shape variance.
dc.description.degreeUndergraduate
dc.identifier.cd600000214
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1205
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleExperimental and Theoretical Verification: Detection of Exact Ten Disease from Real Blood Sample
dc.typeProject
oaire.citation.endPage31
oaire.citation.startPage1
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
600000214-abstract.pdf
Size:
156.77 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
600000214.pdf
Size:
716.93 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.93 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Theses - Undergraduate