This project shows an automated deep learning based computer vision algorithm that can provide a complete blood cell count (i.e. the number red blood cells, number of white blood cells, different types of white blood cells, number of plateletes etc.) in a person's body from a digitized blood smear image of his blood sample. The neural network used here is a modified version of the Unet framework that identifies different type of blood cells and semantically segments them creating the output image where the different cell regions are indicated with different colors. Additionally, it can also detect the presence of malarial pathogens in the blood. The final test accuracy obtained by the network was 93%.