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All Journal Bulletin of Electrical Engineering and Informatics
Mohd Razali Md Tomari
University 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.
Analysis of minimum face video duration and the effect of video compression to image-based non-contact heart rate monitoring system Norwahidah Ibrahim; Mohd Razali Md Tomari; Wan Nurshazwani Wan Zakaria
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (393.601 KB) | DOI: 10.11591/eei.v9i1.1855

Abstract

Heart rate (HR) is one of important indicator for human physiological diagnosis, and camera can be used to detect it via photoplethysmograph (PPG) signal extraction. In doing so, number of sample images required to measure the HR signal, and quality of the images itself are important to yield an accurate reading. This paper tackles such an issue by analyzing the effect of sampling interval to HR reading in compressed and original video format, obtained in various ranging locations. Technically, important facial points from video stream were estimated by using cascade regression facial tracker. Based on the facial points, region of interest (ROI) was constructed where non-rigid movement is minimal. Next, PPG signal was extracted by calculating the average value of green pixel intensity from the ROI. Following that, illumination variation was separated from the signal via independent component analysis (ICA). The PPG signal was further processed using series of signal filtering techniques to exclude frequencies beyond range of interest prior estimate the HR. From the experiment it can be observed that sampling time of 2 seconds in uncompressed video shows promising HR within the range of 1 to 5 meters.
Co-Authors Mohd Norzali Hj Mohd Nor Surayahani Suriani Norwahidah Ibrahim Syadia Nabilah Mohd Safuan Wan Nurshazwani Wan Zakaria Wan Nurshazwani Wan Zakaria