An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis.
In Africa, Uganda is among the countries with a high number of babies (20,000 babies)born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | en_US |
| Published: |
Kabale University
2024
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12493/2000 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1813635241368092672 |
|---|---|
| author | Mabirizi, Vicent Kawuma, Simon Safari, Yonasi |
| author_facet | Mabirizi, Vicent Kawuma, Simon Safari, Yonasi |
| author_sort | Mabirizi, Vicent |
| collection | KAB-DR |
| description | In Africa, Uganda is among the countries with a high number of babies (20,000 babies)born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing the shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied to blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cell images as inputs to detect if these cells are sickle cell anemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity, and specificity
were 98.18%, 98.29%, and 97.98% respectively. |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-2000 |
| institution | KAB-DR |
| language | en_US |
| publishDate | 2024 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-20002024-08-01T00:02:51Z An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. Mabirizi, Vicent Kawuma, Simon Safari, Yonasi Algorithm Detect Overlapping Red Blood Cells Sickle Cell Disease Diagnosis In Africa, Uganda is among the countries with a high number of babies (20,000 babies)born with sickle cell, contributing between 6.8% of the children born with sickle cell every year worldwide and approximately 4.5% of the children born with hemoglobinopathies worldwide. It is estimated that by 2050, sickle cell cases will increase by 30% if no intervention is put in place. To facilitate early detection of sickle cell anemia, medical experts employ machine learning algorithms to detect sickle cell abnormality. Previous research revealed that algorithms for recognizing the shape of a sickle cell from blood smear by fractional dimension, cannot detect sickle cells if applied to blood samples containing overlapping red blood cells. In this research, the authors developed an algorithm to detect overlapping red blood cells for sickle cell disease diagnosis. The algorithm uses canny edge and double threshold machine learning techniques and takes overlapping red blood cell images as inputs to detect if these cells are sickle cell anemic. These images have a scale magnification of (200×, 400×, 650×) pixel taken using a microscope. The algorithm was tested on a total of 1000 digital images and the overall accuracy, sensitivity, and specificity were 98.18%, 98.29%, and 97.98% respectively. 2024-05-27T14:16:05Z 2024-05-27T14:16:05Z 2024 Article Mabirizi, V., Kawuma, S., & Safari, Y. (2022). An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. Kabale: Kabale University. http://hdl.handle.net/20.500.12493/2000 en_US application/pdf Kabale University |
| spellingShingle | Algorithm Detect Overlapping Red Blood Cells Sickle Cell Disease Diagnosis Mabirizi, Vicent Kawuma, Simon Safari, Yonasi An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title | An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title_full | An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title_fullStr | An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title_full_unstemmed | An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title_short | An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. |
| title_sort | algorithm to detect overlapping red blood cells for sickle cell disease diagnosis |
| topic | Algorithm Detect Overlapping Red Blood Cells Sickle Cell Disease Diagnosis |
| url | http://hdl.handle.net/20.500.12493/2000 |
| work_keys_str_mv | AT mabirizivicent analgorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis AT kawumasimon analgorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis AT safariyonasi analgorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis AT mabirizivicent algorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis AT kawumasimon algorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis AT safariyonasi algorithmtodetectoverlappingredbloodcellsforsicklecelldiseasediagnosis |