White blood cell recognition for biomarker model using improved convolutional neural network (CNN)

White Blood Cell (WBC) is one of important elements in protecting human’s immunity system. WBC composed of two main elements namely cytoplasm and nucleus, with almost the same in color contrast, and hence make it hard to be detected and analyzed. Manual WBC analysis is less efficient, therefore a Co...

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Main Author:	Mohd Safuan, Syadia Nabilah
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	R856-857 Biomedical engineering Electronics Instrumentation
Online Access:	http://eprints.uthm.edu.my/8495/1/24p%20SYADIA%20NABILAH%20MOHD%20SAFUAN.pdf http://eprints.uthm.edu.my/8495/2/SYADIA%20NABILAH%20MOHD%20SAFUAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8495/3/SYADIA%20NABILAH%20MOHD%20SAFUAN%20WATERMARK.pdf
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spelling	my-uthm-ep.84952023-04-02T01:10:14Z White blood cell recognition for biomarker model using improved convolutional neural network (CNN) 2022-08 Mohd Safuan, Syadia Nabilah R856-857 Biomedical engineering. Electronics. Instrumentation White Blood Cell (WBC) is one of important elements in protecting human’s immunity system. WBC composed of two main elements namely cytoplasm and nucleus, with almost the same in color contrast, and hence make it hard to be detected and analyzed. Manual WBC analysis is less efficient, therefore a Computer Aided Diagnosis (CAD) based on Deep Learning (DL) model become subject of interest nowadays. However, with vast amount of WBCs data and various DL architectures available, tuning an optimal DL model is a daunting task. In this project, a diagnostic algorithm for WBCs’ DL analysis is proposed by combining transfer learning approach with fine tuning (FT) approach and tested on Kaggle dataset (9957 images). Initially, a transfer learning analysis are conducted using six well known Convolutional Neural Network (CNN) structure which are Alexnet, Googlenet, Densenet, Mobilenet, Resnet and VGG. Next, the CNN model that yield high accuracy with less prone to overfitting is selected for the FT process. Finally, the optimal FT model will undergo series of performance testing using two public WBCs dataset which are LISC and IDB-2. From series of experiments, it can be concluded that the pre-trained AlexNet model gave highest performance with 98.79% training accuracy and 99.10% testing accuracy compare to other models. When implementing the layer refinement analysis, it shows that AlexNet model with 4-layer FT and RMSProp optimizer, significantly improved the performance with 100% accuracy in training and 100% accuracy in testing. Eventually, the FT-ALexNet model tested on LISC database demonstrate 95.52% performance for the true-positive samples, and on IDB-2 100% performance for the true-negative samples. Proposed method (FT-Alexnet) which compares and applies two-stage optimization technique works well with several WBC datasets and able to improve 2.22% from the existing work. 2022-08 Thesis http://eprints.uthm.edu.my/8495/ http://eprints.uthm.edu.my/8495/1/24p%20SYADIA%20NABILAH%20MOHD%20SAFUAN.pdf text en public http://eprints.uthm.edu.my/8495/2/SYADIA%20NABILAH%20MOHD%20SAFUAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8495/3/SYADIA%20NABILAH%20MOHD%20SAFUAN%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	R856-857 Biomedical engineering Electronics Instrumentation
spellingShingle	R856-857 Biomedical engineering Electronics Instrumentation Mohd Safuan, Syadia Nabilah White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
description	White Blood Cell (WBC) is one of important elements in protecting human’s immunity system. WBC composed of two main elements namely cytoplasm and nucleus, with almost the same in color contrast, and hence make it hard to be detected and analyzed. Manual WBC analysis is less efficient, therefore a Computer Aided Diagnosis (CAD) based on Deep Learning (DL) model become subject of interest nowadays. However, with vast amount of WBCs data and various DL architectures available, tuning an optimal DL model is a daunting task. In this project, a diagnostic algorithm for WBCs’ DL analysis is proposed by combining transfer learning approach with fine tuning (FT) approach and tested on Kaggle dataset (9957 images). Initially, a transfer learning analysis are conducted using six well known Convolutional Neural Network (CNN) structure which are Alexnet, Googlenet, Densenet, Mobilenet, Resnet and VGG. Next, the CNN model that yield high accuracy with less prone to overfitting is selected for the FT process. Finally, the optimal FT model will undergo series of performance testing using two public WBCs dataset which are LISC and IDB-2. From series of experiments, it can be concluded that the pre-trained AlexNet model gave highest performance with 98.79% training accuracy and 99.10% testing accuracy compare to other models. When implementing the layer refinement analysis, it shows that AlexNet model with 4-layer FT and RMSProp optimizer, significantly improved the performance with 100% accuracy in training and 100% accuracy in testing. Eventually, the FT-ALexNet model tested on LISC database demonstrate 95.52% performance for the true-positive samples, and on IDB-2 100% performance for the true-negative samples. Proposed method (FT-Alexnet) which compares and applies two-stage optimization technique works well with several WBC datasets and able to improve 2.22% from the existing work.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd Safuan, Syadia Nabilah
author_facet	Mohd Safuan, Syadia Nabilah
author_sort	Mohd Safuan, Syadia Nabilah
title	White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
title_short	White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
title_full	White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
title_fullStr	White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
title_full_unstemmed	White blood cell recognition for biomarker model using improved convolutional neural network (CNN)
title_sort	white blood cell recognition for biomarker model using improved convolutional neural network (cnn)
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/8495/1/24p%20SYADIA%20NABILAH%20MOHD%20SAFUAN.pdf http://eprints.uthm.edu.my/8495/2/SYADIA%20NABILAH%20MOHD%20SAFUAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8495/3/SYADIA%20NABILAH%20MOHD%20SAFUAN%20WATERMARK.pdf
_version_	1776103359235751936

White blood cell recognition for biomarker model using improved convolutional neural network (CNN)

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