Cross validation analysis of convolutional neural network variants with various white blood cells datasets for the classification task

Mohd Safuan, Syadia Nabilah and Md Tomari, Mohd Razali and Wan Zakaria, Wan Nurshazwani (2022) Cross validation analysis of convolutional neural network variants with various white blood cells datasets for the classification task. International Journal of Online and Biomedical Engineering, 18 (2). pp. 123-140.

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Abstract

White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation�classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG�16 and skip connection based model (Resnet & Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experi�ments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets.

Item Type:	Article
Uncontrolled Keywords:	Computer aided system; convolutional neural network; white blood cell
Subjects:	T Technology > TP Chemical technology > TP248.13-248.65 Biotechnology
Divisions:	Faculty of Electrical and Electronic Engineering > Department of Electronic Enngineering
Depositing User:	Mr. Abdul Rahim Mat Radzuan
Date Deposited:	12 Apr 2022 06:36
Last Modified:	12 Apr 2022 06:36
URI:	http://eprints.uthm.edu.my/id/eprint/6898

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