Blood Cells Counting and Localisation through Deep Learning Object Detection

Nowadays pathologists have to analyse blood cells manually with the aim to diagnose diseases. In order to perform this manual task blood samples must be collected from the patient and then placed on a microscope slide. This slide is studied with the aim to detect the abnormality presence. The manual method used to identify abnormality of blood cells is tedious, prone to human errors and time consuming. Hence there is a need for computer aided system which can analyze blood cells automatically at faster rate with accuracy. Such systems can be designed using image processing techniques. To automatise this process by helping pathologists, in this paper we propose the adoption of deep learning to automatically count and localise red blood cells, white blood cells and platelets by analysing blood microscopic images. We resort to the YOLO object detection model, able to look at the whole image so its predictions are informed by global context in the image. To show the effectiveness of the proposed method we evaluate our model on a dataset composed by 874 microscopic blood images, obtaining interesting results. Furthermore we show several examples related to how the proposed method can be helpful for the pathologists in their real-world work.