The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0111058
PDF

White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

Author 1: Nurasyeera Rohaziat
Author 2: Mohd Razali Md Tomari
Author 3: Wan Nurshazwani Wan Zakaria
Author 4: Nurmiza Othman

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: There are several types of blood cancer. One of them is Leukaemia. This is due to leukocyte or white blood cell (WBCs) production problem in the bone marrow. Detection at earlier stage is important so that the patient is able to get a proper treatment. The conventional detection and blood count method is less efficient and it is done manually by pathologist. Thus, there will be a long line to wait for the results and also delay the treatment. A faster detection procedure and technique will have high impact on the real time diagnostic. Fortunately, these problems are able to overcome by making the blood test procedures automatic. One of the effort is the development of deep learning for WBCs detection and classification. In computer aided WBCs detection, the You Only Look Once (YOLO) based platform present a promising outcome. However, the investigation of optimal YOLO structure remains vague. This paper investigate the effect of the deep learning based WBCs detection using You Only Look Once version 3 (YOLOv3) with different pretrained Convolutional Neural Network (CNN) model. The models that been tested are the Alexnet, Visual Geometry Group 16 (VGG16), Darknet19 and the existing YOLOv3 feature extraction model, the Darknet53. The architecture consist of the bounding box for class prediction, feature extraction, and additional convolutional layers. It was trained with 242 WBCs images from Local Initiatives Support Corporation (LISC) dataset. The final outcome shows that the YOLOv3 architecture with Alexnet as its feature extractor produced the highest mean average precision of 98% and have better performance than the other models.

Keywords: Alexnet; darknet19; darknet53; detection; VGG16; white blood cells; YOLO

Nurasyeera Rohaziat, Mohd Razali Md Tomari, Wan Nurshazwani Wan Zakaria and Nurmiza Othman, “White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111058

@article{Rohaziat2020,
title = {White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111058},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111058},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {10},
author = {Nurasyeera Rohaziat and Mohd Razali Md Tomari and Wan Nurshazwani Wan Zakaria and Nurmiza Othman}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org