WBC Analysis using Data Augment Method and Convolutional Neural Network

Abstract

WBC is a White Blood Cell or White Blood Corpuscle also known as Leucocytes. The normal count of WBC ranges from 4000 to 11000/mm3. It plays a vital role in the human body. Many diseases in human start with the abnormal balance of WBC, which acquire the part of immunity. To have adequate knowledge about WBC, we have to have a clinical test like blood count test which gives the count of RBC, hemoglobin, WBC, etc. RBC is otherwise known as Erythrocyte and it does not have a nucleus, with pigment hemoglobin. Due to the presence of this pigment, blood is red in color. In RBC, O2 and CO2 are transported in and out of the tissues. Recent research explains about diseases like cancer, allergy, breast cancer, etc are caused due to lack or abnormal WBC. This comes with the solution of finding the count of WBC in two types: Manually and automated way. In our paper, we are concentrating on collecting the WBC count using the Data augmentation method and Convolutional Neural Network.The Quality of image is improved in comparison with number of augmented images. This explains that we have 12500 sample images in the dataset in which 9957 samples are trained, validated on 2487 samples and training accuracy is high with increasing epoch value.