Non-Invasive Blood Group Detection using Nir Sensor
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Abstract
The need for an economical Blood Group measuring solution is widespread, and it is especially critical in developing nations. The most widely used approach in both resource-rich and resource-limited environments is image processing, which would be an appropriate choice for this solution. In Invasive method to determine the blood type the patient blood sample is collected by pricking the fingertip of the patient, it may cause infection and pain to the patient. The aim of the proposed system is to provide a non-invasive Blood Group measurement processes using image processing and machine learning algorithm. In order to compute blood groups, it is also examined how different data collecting locations, bio signal processing methods, theoretical underpinnings, the photoplethysmogram (PPG) signal and feature extraction procedure, image processing algorithms, and detection models differ. The results of this study were then utilized to suggest practical methods for developing a non-invasive point-of-care tool for Blood Group measurement based on Image Processing. Blood group identification is required prior to blood transfusions in emergency conditions. It is done before a blood transfusion in an emergency or when checking a person's blood group for donation. Currently, lab personnel perform tests manually in the laboratory. This takes time and may result in human mistake when determining blood type. The goal of the study survey is to use image processing to reduce the amount of physical labor required to identify blood groups. The presence or absence of agglutination reaction of blood with antigen will be used to determine the blood group.
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References
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