Image Classification of White Blood Cells Using Convolutional Neural Network

DOI

10.2991/978-94-6463-684-0_20How to use a DOI?

Keywords

White Blood Cells; Albumentations; Convolutional Neural Networks; Small Inception

Abstract

Classification of white blood cells (WBCs) is a crucial process in medical diagnosis and research. Automated image classification of white blood cells using machine learning techniques provides faster and more accurate results compared to manual procedures. Convolutional Neural Networks (CNNs) are deep neural systems widely used in the medical classification tasks since they are excellent in feature extraction. This paper used the small Inception or MiniGoogleNet, a simple CNN, to classify five types of white blood cells, namely, basophils, eosinophils, lymphocytes, monocytes, and neutrophils. The quality of the dataset used to train, validate, and test the CNN classifier highly affects its performance. Hence, this paper presents a dataset preprocessing system that involves image processing to improve the quality of images and data augmentation using Albumentations to solve the problem of the highly imbalanced dataset. The model was trained from scratch using the Pytorch library and achieved an accuracy of 97.65%, recall of 94.12%, precision of 94.17%, and F1 score of 94.12%.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Juliet Cagampang
AU  - Ligaya Leah Figueroa
PY  - 2025
DA  - 2025/04/30
TI  - Image Classification of White Blood Cells Using Convolutional Neural Network
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2024)
PB  - Atlantis Press
SP  - 322
EP  - 331
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-684-0_20
DO  - 10.2991/978-94-6463-684-0_20
ID  - Cagampang2025
ER  -