Disease estimation using robust AI methods

Abstract

Human blood scrutinization is an indispensable step to analyze a particular health condition, comprise of a complete blood cell (CBC) count. CBC accentuates the counting of White blood cells (WBCs), red blood cells (RBCs), and Platelets which are implicitly significant for the analysis of severe maladies such as leukemia, thrombocytopenia, and anemia. Traditional approaches like manual counting and automated analyzer were extensively used, which is monotonous, time intensive, and entail a lot of medical experts. To get rid of aforesaid leisure techniques, here by using a machine learning-based object detection and classification algorithm you only look once (YOLO) to count the blood cells. YOLO with modified configuration has been trained on the customized dataset to detect the WBCs, RBCs, and platelets.