Identification and Counting of Blood Cells Using Machine Learning and Image Processing

Abstract

Doctors view the total number of leukocytes in a person’s blood as a crucial sign of that individual’s general health. Historically, blood cell counting was performed manually using a hemocytometer and a few more lab equipment and chemicals. The process is slow and laborious. Red blood cells (RBC), white blood cells (WBC), and platelets are the three kinds of blood cells that can be detected and counted automatically using picture segmentation and S-CNN (Suit-Convolutional Neural Network) machine learning algorithms, as demonstrated in this body of work. Red blood cells, white blood cells, and platelets may be located and counted automatically using an open-source database of blood smear pictures. When the trained model was evaluated using smear images from a distinct dataset, it was observed that the learned models were relatively simplistic. The computer-aided tracking and identification technique allows us to count blood cells in less than one second from photographs of character assassination. It is helpful for real-world applications and has an approximate 92% degree of accuracy.