An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis.

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dc.contributor.authorMabirizi, Vicent
dc.contributor.authorKawuma, Simon
dc.contributor.authorSafari, Yonasi
dc.date.accessioned2024-05-27T14:16:05Z
dc.date.available2024-05-27T14:16:05Z
dc.date.issued2024
dc.description.abstractIn 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.
dc.identifier.citationMabirizi, V., Kawuma, S., & Safari, Y. (2022). An algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis. Kabale: Kabale University.
dc.identifier.urihttp://hdl.handle.net/20.500.12493/2000
dc.language.isoen_US
dc.publisherKabale University
dc.subjectAlgorithm
dc.subjectDetect
dc.subjectOverlapping
dc.subjectRed Blood Cells
dc.subjectSickle Cell
dc.subjectDisease Diagnosis
dc.titleAn algorithm to Detect Overlapping Red Blood Cells for Sickle Cell Disease Diagnosis.
dc.typeArticle

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