Blood cell detection from image using Mask-RCNN

A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment’s and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using Mask-RCNN image classification algorithm. Mask-RCNN framework has been trained with BCCD Dataset of blood smear images to automatically identify red blood cells, white blood cells, and platelets.