N-BGP (Noninvasive Blood Group Prediction Dataset)

This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process. In typical pathological blood test, when blood sample is collected by pricking the skin, it may originate bleeding, lead to fainting and can cause skin laceration at certain part of body. The proposed deep learning approach is a non-invasive without perforating the skin that automatically predict the human blood type by applying deep learning algorithms to the captured images of superficial blood vessels present on finger. As laser light passes, the optical image of blood vessels hidden on the finger skin surface area is captured, which incorporates specific antigen shapes such as antigen ‘A’ and antigen ‘B’ present on the surface. Captured shapes of different antigen further used to predict the blood group of humans. The system requires high-definition camera to capture the antigen pattern from the red blood cells surface for classification of blood type without piercing the skin of patient. The proposed solution is easy to implement, straightforward and significant to identify ABO blood group instantly. It provides cost effective solution for identifying blood group in case of medical emergencies, battleground and more useful for infants.