2020
DOI: 10.11591/ijeecs.v18.i2.pp717-723
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Microcontroller based in vitro hematocrit measurement system

Abstract: <span>The hematocrit (HCT) is the most important measurement in the blood profile. It has been used for early diagnose of the specific blood diseases such as anaemia, leukaemia and malaria. The microhematocrit is the conventional method of measurement of HCT manually which is time-consuming and uncertain due to human error. An automated system for measuring hematocrit will minimize the human-error and the time which will give the ability for medical staff to serve more patients. This paper aims to demons… Show more

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Cited by 3 publications
(2 citation statements)
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“…It is also used as the core for measuring the vital signs of the user's since its specifications are suitable for real-time processing [19], [20]. Microcontroller boards such as Arduino have also been widely used for instrumentations and control applications in the medical field [21]- [30]. User interactions, triage processes, and report generations are controlled by desktop application, from which a triage report in portable document format (PDF) format will be produced and emailed to the medical operator.…”
Section: System Designmentioning
confidence: 99%
“…It is also used as the core for measuring the vital signs of the user's since its specifications are suitable for real-time processing [19], [20]. Microcontroller boards such as Arduino have also been widely used for instrumentations and control applications in the medical field [21]- [30]. User interactions, triage processes, and report generations are controlled by desktop application, from which a triage report in portable document format (PDF) format will be produced and emailed to the medical operator.…”
Section: System Designmentioning
confidence: 99%
“…Moving from human inspection to machine inspection will impose a new approach in the medical field to analyze the multilane classification and the disease conditions [8]. The strategy of implementing deep learning (DL) approach for the disease recognition and image distinction requires a primary training set which is annotated with those lesion symptoms.…”
Section: Introductionmentioning
confidence: 99%