Red Blood Cells Abnormality Classification: Deep Learning Architecture versus Support Vector Machine

The most common and dangerous defect of red blood cells (RBCS) is shape abnormality, The primary detection and confirmation of anaemic stage(shape abnormality) is based on haemoglobin level or manual microscopic examination of peripheral blood smears. The problem of classifying the abnormal cells manually under microscope is that it consumes time, working on huge number of sample manually is burdensome which leads to poor result quality with unnecessary medication leading to life trait to the patient and cause eye fatique to the technicians. This paper proposed a method to classify Rbc's abnormalities based on deformed shaped RBCs image by using SVM and Deep learning in comparison on the RBCs cell Classification. Classifying normal cells of RBCs indicate a healthy patient and Classifying anemic abnormalities indicate presence of disease. And is very important in medical field to detect and classify disease in early stage because it saves and protects human lives. The patients waiting time for blood test is longer because the time taken to generate the result of the blood test is more due to high demand and less equipment. This lead to comparison of the two classifiers in order to predict the one that will best perform on RBCs in order to achieved maximum accuracy for the classification. This study shows that SVM classifier can classify the cells in all condition either small or large dataset while deep learning performs mainly on large and very large dataset which RBCs dataset will be generated in large amount in order to work successfully with the state of the earth on RBCs deformity.