Email this article Swarm intelligence–driven mobilenet optimization for breast cancer classification in ultrasound images
Abstract
This study examines the effectiveness of swarm intelligence algorithms for optimizing MobileNet hyperparameters in breast cancer classification using ultrasound images (BCMID). Three optimization methods—Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and the Whale Optimization Algorithm (WOA)—were applied to identify optimal learning rates, dropout rate, and the optimizer. The best hyperparameter sets discovered by each algorithm were used to retrain MobileNet to verify consistency and performance stability. The dataset consisted of clinically annotated breast ultrasound images representing benign, malignant, and normal cases. Model performance was assessed using accuracy, macro-precision, macro-recall, and macro-F1-score. The optimized models outperformed the baseline configuration, with ABC achieving 62%, PSO achieving 66%, and WOA achieving 62%. In terms of computational time, PSO required 7710 seconds, ABC 14,148 seconds, and WOA 7622 seconds, highlighting notable differences in optimization efficiency. These findings demonstrate that swarm-based optimization can enhance MobileNet’s diagnostic performance while exhibiting varying computational costs, offering a reliable framework for computer-aided breast cancer detection in ultrasound imaging.
Received on, 15 November 2025
Accepted on, 24 November 2025
Published on, 22 December 2025
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DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1799
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Copyright (c) 2025 Marwa A. ElShenawy, Rania Kadry
Advances in Computing and Engineering
E-ISSN: 2735-5985
P-ISSN: 2735-5977
Published by:
Academy Publishing Center (APC)
Arab Academy for Science, Technology and Maritime Transport (AASTMT)
Alexandria, Egypt
