Blood Cell Disease Prediction with Machine Learning

Abstract

Our body has cells that provide oxygen to our cell nucleus, called red blood cells (RBC).  The other cells that are fighting the disease in our body are called the White Blood Cell (WBC), and some types of cells prevent bleeding with clotting processes. There are three main types of blood cells. If we have some disorder in these cells, then our body faces diseases like leukemia, lymphoma and myeloma. Blood cell diseases can be identified using blood cell image symptoms, causes, and present conditions. Blood smear images with surrounding images of red, platelet, and white cell breakdown play an important role in assessing and diagnosing a large range of illnesses, including infection, disease, and leukemia. Due to the segmentation or breakdown procedures, some image processing techniques are performed to improve picture detection quality. Blood cell segmentation remains a serious challenge. That’s why deep learning technology is used to get the blood cell images clearly with the application of cutting-edge technology for the white blood cells, platelets and red blood cells in blood smear images. The accuracy of blood cell smear images required for automatic illness identification has been demonstrated in previous research experiments. To tackle this blood cell disease issue and optimize the system's performance to detect the diseases present in blood cells, we can find through their images by proposing deep learning.