Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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A DenseNet-EfficientNet Ensemble Framework for Automated Leukemia Classification

Authors
Mouna Saadallah1, *, Farah Bennaoum1, Latefa Oulladji1, Mohamed Nazim Ben-Naoum2
1Evolutionary Engineering and Distributed Information Systems Laboratory, Department of Computer Science, Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, Algeria, 22000
2Head of Unit, Department of Hemobiology and Blood Transfusion, EHU Oran, Medicine Faculty, Oran1 University of Oran, Oran, Algeria, 31000
*Corresponding author. Email: mouna.saadallah@univ-sba.dz
Corresponding Author
Mouna Saadallah
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_4How to use a DOI?
Keywords
Leukemia Classification; DenseNet201; EfficientNetB3; Medical Imaging; Ensemble Techniques
Abstract

Early and precise diagnosis of leukemia subtypes directly impacts the determination of optimal treatment strategies and patient survival rates. Traditional methods often rely on manual microscopic examination of blood and bone marrow samples, which can be time-consuming and prone to human error. In this paper, we propose a comprehensive and innovative approach combining the DenseNet201 and the EfficientNetB3 architectures through the stacking and weighted average Ensemble techniques to classify six types of Leukemia: Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), Acute Lymphoblastic Leukemia (L1 and L2), and Acute Myeloid Leukemia (M0 and M1). The four models were trained and evaluated on a diverse dataset of microscopic images. The Ensemble techniques demonstrated superior performance against the standalone models, achieving a peak precision of 99.56%, further proving the efficiency and reliability of deep learning architectures in the development of accurate, and reliable computer-aided diagnosis systems for automated leukemia classification, that can reduce the workload on pathologists.

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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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_4How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Mouna Saadallah
AU  - Farah Bennaoum
AU  - Latefa Oulladji
AU  - Mohamed Nazim Ben-Naoum
PY  - 2025
DA  - 2025/08/05
TI  - A DenseNet-EfficientNet Ensemble Framework for Automated Leukemia Classification
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 21
EP  - 31
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_4
DO  - 10.2991/978-94-6463-805-9_4
ID  - Saadallah2025
ER  -