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Blood Group Detection Using Deep Learning and Image Processing

EasyChair Preprint no. 8391

4 pagesDate: July 5, 2022


Platelet division and counting are thought to be significant advances that aids in separating highlights for research into a variety of diseases. The guidance counting of RBCs in minuscule images are an incredibly arduous, time-consuming, and nasty basic operation. Hematologist practitioners can conduct studies more quickly and precisely thanks to planned research. The determination of blood type is an important step in the healing process for any treatment. False blood transfusions will cause a slew of problems. This framework provides simple and quick ways for non-obtrusively identifying evidence of blood categories and Rhesus elements. Our shape is based on some actual informational compilations of several human finger-tip character photos. Blood types are classified based on the presence or absence of certain natural materials known as antibodies and the presence or absence of received antigenic protein materials on the surfaces of erythrocytes within the body. Blood gatherings can be arranged along those lines by utilizing the optical properties of the antigens and the rhesus calculates the gift of the blood.

Keyphrases: Blood group, Blood Types, GLCM, Image Processing Techniques, Rapid test

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {B.K Aishwarya and Pollishetti Abhinav and Pariki Rama Krishna and Abhishek Medikonda},
  title = {Blood Group Detection Using Deep Learning and Image Processing},
  howpublished = {EasyChair Preprint no. 8391},

  year = {EasyChair, 2022}}
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