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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Syadia Nabilah Mohd Safuan
Universiti Tun Hussein Onn Malaysia
Electrical & Electronics Engineering

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Investigation of white blood cell biomaker model for acute lymphoblastic leukemia detection based on convolutional neural network Syadia Nabilah Mohd Safuan; Mohd Razali Md Tomari; Wan Nurshazwani Wan Zakaria; Mohd Norzali Hj Mohd; Nor Surayahani Suriani
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (855.675 KB) | DOI: 10.11591/eei.v9i2.1857

Abstract

Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by uncontrollable growth of malignant and immature White Blood Cells (WBCs) which is called lymphoblast. Traditionally, lymphoblast analysis is done manually and highly dependent on the pathologist’s skill and  experience which sometimes yields inaccurate result. For that reason, in this project an algorithm to automatically detect WBC and subsequently examine ALL disease using Convolutional Neural Network (CNN) is proposed. Several pretrained CNN models which are VGG, GoogleNet and Alexnet were analaysed to compare its performance for differentiating lymphoblast and non-lymphoblast cells from IDB database. The tuning is done by experimenting the convolution layer, pooling layer and fully connected layer. Technically, 70% of the images are used for training and another 30% for testing. From the experiments, it is found that the best pretrained models are VGG and GoogleNet compared to AlexNet by achieving 100% accuracy for training. As for testing, VGG obtained the highest performance which is 99.13% accuracy. Apart from that, VGG also proven to have better result based on the training graph which is more stable and contains less error compared to the other two models.
Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia Syadia Nabilah Mohd Safuan; Mohd Razali Md Tomari; Wan Nurshazwani Wan Zakaria; Mohd Norzali Haji Mohd; Nor Surayahani Suriani
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i2.pp597-607

Abstract

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 hematology 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.
Co-Authors Mohd Norzali Haji Mohd Mohd Norzali Hj Mohd Mohd Razali Md Tomari Mohd Razali Md Tomari Nor Surayahani Suriani Wan Nurshazwani Wan Zakaria