INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING VOL.10 NO. 7 (2018) 34–42 © Universiti Tun Hussein Onn Malaysia Publisher’s Office IJIE Journal homepage: http://penerbit.uthm.edu.my/ojs/index.php/ijie The International Journal of Integrated Engineering *Corresponding author: hajabdulkarim@jigpoly.edu.ng 2018 UTHM Publisher. All right reserved. penerbit.uthm.edu.my/ojs/index.php/ijie 34 Red Blood Cells Abnormality Classification: Deep Learning Architecture versus Support Vector Machine Hajara Abdulkarim Aliyu 1,2,* , Rubita Sudirman 2 , Mohd Azhar Abdul Razak 2 , Muhamad Amin Abd Wahab 2 1 Jigawa State Polytechnic Dutse, Kiyawa Road, Jigawa State, 7040, Nigeria, 2 Universiti Teknologi Malaysia, Faculty of Electrical Engineering, Johor Bahru, 81310, Malaysia * Corresponding Author DOI: https://doi.org/10.30880/ijie.2018.10.07.004 Received 26 October 2018; Accepted 15 November 2018; Available online 30 November 2018 1. Introduction The human blood cell is comprise of the three major components of blood cells that are white blood cell (WBC), platelets and red blood cell (RBC). The RBCs are majority of cells in human body and it has many functions in human body, like moving oxygen round the body, carrying waste and carbon dioxide products away from tissue and cells. The normal shape of RBCs are biconcave disk with 7 to 8μm in cell diameter and 2.2 μm thickness (Aliyu, 2017). The RBCs abnormal morphological nature of the cells gives anemia sign, hemoglobin reduction (the protein that bind with an oxygen molecule in RBCs), also the secondary effect of many other disorders. Considering medical perspective, the diagnosis of RBC gives more information on various related blood cell diseases. For example, the shape of the RBCs with its deformity has connection to the relevant disease more especially anemia and the secondary effect of several other disorder (Webster, & Cazzanti, 2004). Approximately 24.5% of the world population are affected with anemia and other related blood disorders. This makes most pathological laboratories to used visually inspection of the blood smear slide under the microscope. The method is expensive, time consuming, laborious, and need skilled technicians (Dalvi & Vernekar, 2016). Abstract: 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. Keywords — Red blood cells (RBCs); Deep Learning; SVM; Rbc’s abnormality