Leukemia Classification through Deep Learning Techniques and Generative AI

Malik, Ibrahim, Diang'A, Lathifa Jaffer, Stynes, Paul, Pathak, Pramod and Sahni, Anu (2025) Leukemia Classification through Deep Learning Techniques and Generative AI. In: 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). IEEE, Antalya, Turkiye, pp. 1-6. ISBN 979-833153562-9

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Abstract

Leukemia is a cancer originating in the bone marrow and leads to rapid proliferation of abnormal blood cells. The main objective of this study is to implement a Convolutional Neural Network (CNN) to detect and classify leukemia from microscopic cell images. The proposed framework combines a Generative Adversarial Network (GAN) that generates synthetic images of healthy cells to address class imbalance and training on a balanced leukemia dataset, with four different CNN architectures (InceptionV3, ResNet50, EfficientNetB3 and InceptionV4) - the effectiveness of this approach is validated on a Breast Cancer tumor dataset consisting of ultrasound images. Unlike prior studies that rely on standard augmentation, our approach incorporates synthetic image quality metrics (FID, IS, SSIM) to validate realism and structural fidelity.The results reveal GAN architecture achieving 16% higher performance on cell images compared to tumor images. Additionally, results obtained for each model were 76%, 80%, 75%, and 75% respectively, with RestNet50 attaining the best result. Obtained results underline potential contribution of deep learning in cancer detection and improving clinical outcomes through GAN-augmentation, addressing class imbalance effectively.

Item Type:	Book Section
Uncontrolled Keywords:	CNNs; Deep Learning; GANs; Generative AI; Leukemia Classification
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RB Pathology R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	22 Oct 2025 13:50
Last Modified:	22 Oct 2025 13:50
URI:	https://norma.ncirl.ie/id/eprint/8865

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