Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

Authors

  • Syadia Nabilah Mohd Safuan Universiti Tun Hussein Onn Malaysia
  • Mohd Razali Md Tomari Universiti Tun Hussein Onn Malaysia
  • Wan Nurshazwani Wan Zakaria Universiti Tun Hussein Onn Malaysia

DOI:

https://doi.org/10.3991/ijoe.v18i02.27321

Keywords:

White Blood Cell, convolutional neural network, computer aided system

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 experiments, 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.

Author Biographies

Syadia Nabilah Mohd Safuan, Universiti Tun Hussein Onn Malaysia

Faculty of Electrical and Electronic Engineering

Mohd Razali Md Tomari, Universiti Tun Hussein Onn Malaysia

Principle Researcher in Research Centre for Applied Electromagnetics

Wan Nurshazwani Wan Zakaria, Universiti Tun Hussein Onn Malaysia

Faculty of Electrical and Electronic Engineering

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Published

2022-02-16

How to Cite

Mohd Safuan, S. N., Md Tomari, M. R., & Wan Zakaria, W. N. (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 (iJOE), 18(02), pp. 123–140. https://doi.org/10.3991/ijoe.v18i02.27321

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Papers