SE-CNN: A LIGHTWEIGHT DEEP LEARNING MODEL FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION
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Squeeze and Excitation, Convolutional Neural Network, Medical image classification, Acute Lymphoblastic Leukemia, Peripheral blood smear
Abstract
Leukemia is a potentially fatal hematological cancer that affects people of all ages and remains one of the leading causes of cancer death worldwide. It mostly impacts white blood cells (WBCs), which leads to the aberrant growth of immature lymphocytes that disrupt the bloodstream and bone marrow. Convolutional Neural Networks (CNNs), a type of deep learning, have become a potent instrument in medical image analysis in recent years, offering significant potential for automated and accurate disease classification. In this study, we present a lightweight CNN architecture enhanced with Squeeze-and-Excitation (SE) blocks for the classification of leukemia from peripheral blood smear images. The proposed model effectively captures channel-wise interdependencies to dynamically recalibrate feature responses, thereby improving the discrimination of relevant features. There were two publicly accessible datasets used for the experiments: C-NMC-2019 and Blood Cells Cancer (Acute Lymphoblastic Leukemia – ALL). The proposed model achieved an accuracy of 97.12% on the C-NMC-2019 dataset (binary classification) and 99.79% on the Blood Cells Cancer (ALL) dataset (multi-class classification). The results demonstrate that the proposed architecture achieves a strong trade-off between computational efficiency and classification accuracy, indicating its suitability for real-time, computer-aided leukemia diagnosis in clinical settings.
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References
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Copyright (c) 2026 Awaz Mustafa Abbas, Maiwan Bahjat Abdulrazaq, and Adel AL-Zebari
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