Red Blood Cell Classification: Deep Learning Architecture Versus Support Vector Machine

In medical field, the classification of red blood cells (RBCs) are used as an indicator to classify the type of abnormality presence in RBCs. The problem of classifying abnormal cells manually such as achantocyte, sickle cell, elliptocyte, tear drop and normal healthy cell under the microscope tend to give inaccurate result and errors. This paper proposed a method to classify 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 achanthocyte, sickle cell, elliptocyte, teardrop cells 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 suggested that SVM classifier outperformed deep learning classifier because the SVM can classify the cells in all condition either small or large dataset while deep learning performs mainly on large dataset only.