Blood group detection is a crucial aspect in healthcare industry, especially during blood transfusions,
organ transplantation, and prenatal care. Traditionally, blood typing has been achieved through
serological tests. These are highly accurate tests but require a blood sample and are usually carried out
in a laboratory. It can be invasive, take a lot of time, and relies on specialized equipment; hence, they
are not easily accessible, especially to remote or resource-limited areas. This paper introduces a new
approach for blood group detection through fingerprint image processing. Instead of depending on a
blood sample we take, we look to utilize the uniqueness in our fingerprints — that is, ridge patterns and
minutiae points — for determination of our blood type. Fingerprints are known to possess unique features
that have, upon careful analysis, indicated a possible link to blood group traits. This method, using
advanced image processing techniques and machine learning algorithms, particularly Convolutional
Neural Networks (CNNs), can analyze fingerprint images and predict blood types with accuracy. It could
transform the way blood typing is done by offering a non- invasive, quick, and affordable alternative
that could be used in places where traditional blood typing is very challenging. The preliminary data
look encouraging, indicating the potential of this approach for revolutionizing point-of-care diagnostics
and making blood typing easier and more efficient and quick.
