Indonesian Journal of Electrical Engineering and Computer Science Vol. 14, No. 2, May 2019, pp. 597~607 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v14.i2.pp597-607  597 Journal homepage: http://iaescore.com/journals/index.php/ijeecs Computer aided system for lymphoblast classification to detectacute lymphoblastic leukemia Syadia Nabilah Mohd Safuan, Mohd Razali Md Tomari, Wan Nurshazwani Wan Zakaria, Mohd Norzali Haji Mohd, Nor Surayahani Suriani Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Malaysia Article Info ABSTRACT Article history: Received Sep 26, 2018 Revised Nov 28, 2018 Accepted Jan 20, 2019 Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis which is challenging and time consuming. It can also yield inaccurate result as it is highly dependent on the pathologist’s skills. Industry has come out with hemat ology counter which is fast, accurate and automated. However, these machines are costly and cannot be afforded by some countries. For that reason, Computer Aided System (CAS) will be a great help to the pathologist for assisting purposes and it also can act as second opinion for the pathologist. This system contains six main steps which are color space correction, WBC segmentation, post processing, clumped area extraction, feature extraction and lymphoblast classification. Firstly, color space correction is apply by using l*a*b* color space to standardize the image’s intensity. Next, WBC segmentation is made to prune out WBC region using color space analysis with Otsu thresholding. However, segmented image contains noises that need to be eliminated and it is accomplished by applying morphological filter with Connected Component Labelling (CCL). There is an overlapping WBC which need to be separated by using Watershed method to extract the individual cells. Next, feature extraction is made to collect the cell’s data to be fed into the classifier. Classifier used in this system to classify lymphoblast is Support Vector Machine (SVM) and this system is able to achieve 96.69% of accuracy. Keywords: Acute lymphoblastic leukemia Color space analysis Support vector machine White blood cell Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Mohd Razali Md Tomari, Department of Mechatronic and Robotic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia. Email: mdrazali@uthm.edu.my 1. INTRODUCTION Three main elements in a blood are Red Blood Cell (RBC), White Blood Cell (WBC) and platelet. We are exposed to many viruses and bacteria almost in every place. These situations can lead to sickness and death if human immune system is not strong to fight the viruses. In this case, WBC plays a very important role as its function is to fight bacteria and viruses in human body [1]. Having a good immune system is crucial to help fight sickness by monitoring the analysis of WBC in human body. Diseases such as HIV and Lymphoma can be diagnosed by low WBC count and diseases such as Leukemia and Anemia can be detected by a high WBC count. Leukemia is a very serious disease that can contribute to the percentage of death worldwide.