Engineering & Technology
Received: 01 Jun 2018 , Published: 03 June 2018
Views: 109 , Download: 64
|1||BHARAT R. BHALSHANKAR|
In this paper we present a method to human blood type detection using raspberry pi 3 module. we used different image processing techniques & some software module to image processing. the simple small module which containing of raspberry pi 3 , web camera , keypad, lcd display, etc. is very much efficient for blood type detections on large scale.we proposed the below system for same application.
Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. This paper presents a new methodology for determine blood group by taking image of blood sample contend added chemical such as Anitclonal A, B, D and by processing this image in raspberry pi 3 we can get the blood group. Phenotyping based on Raspberry pi and python 3.5 for detection of blood group using image processing. The image processing techniques such as thresholding and morphological operations are used for the basic operation on the images. By using the standalone system based on the raspberry pi we can easily take the image of blood sample process it and the result will be shown on the LCD. Depending on the agglutination rate we can classify the blood group. Thus, the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation and also used when number of people are large.
 “Image Processing, Analysis and Machine Vision” by Milan Sonka, Vaclav Hlavac and Roger Boyle
 “Fundamentals of Digital Image Processing” by Anil K. Jain
 “Digital Image Processing” by Rafael C. Gonzalez and Richard E. Woods “Blood Groups” by E. Reisner
 International Journal of Computer Applications (0975 – 8887) Volume 157 – No 1, January 2017 “Determination and Classification of Blood Types using Image Processing Techniques”