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<p>Breast cancer (BC) is the second‑leading cause of cancer‑related mortality among women worldwide. Accurate and early classification of mammographic lesions is therefore crucial for improving patient prognosis. In this study, we present a hybrid Walrus Particle Swarm Optimisation (WPS) algorithm that combines the velocity‑guided global search of Particle Swarm Optimisation (PSO) with the cooperative exploitation strategy of the Walrus Optimiser (WO). The proposed WPS is employed to tune the hyper‑parameters of a convolutional neural network enhanced with Swapping of Proficiency (CNN‑SP) for BC image classification. Numerical tests on the 29 CEC‑2017 benchmark functions demonstrate that WPS consistently reaches near‑optimal solutions, validating its exploration–exploitation balance. When applied to the CBIS‑DDSM and MIAS datasets, WPS‑CNN‑SP achieved better results than recent works. In particular, it achieved an Area Under the receiver-operating-characteristic Curve (AUC) = 98.28%, and an Accuracy = 98.99% on CBIS‑DDSM and AUC = 99.76% with Accuracy = 98.99% on MIAS dataset. These findings confirm the potential of WPS as a fast, reliable optimiser for computer‑aided BC screening systems.</p>

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Classification of breast cancer using hybridization version of walrus and particle swarm optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Bhawna Utreja,
  • Reecha Sharma,
  • Amit Wason,
  • Sailesh Iyer,
  • Mueen Uddin,
  • Abdulhalim Dandoush,
  • Mamoon M. Saeed

摘要

Breast cancer (BC) is the second‑leading cause of cancer‑related mortality among women worldwide. Accurate and early classification of mammographic lesions is therefore crucial for improving patient prognosis. In this study, we present a hybrid Walrus Particle Swarm Optimisation (WPS) algorithm that combines the velocity‑guided global search of Particle Swarm Optimisation (PSO) with the cooperative exploitation strategy of the Walrus Optimiser (WO). The proposed WPS is employed to tune the hyper‑parameters of a convolutional neural network enhanced with Swapping of Proficiency (CNN‑SP) for BC image classification. Numerical tests on the 29 CEC‑2017 benchmark functions demonstrate that WPS consistently reaches near‑optimal solutions, validating its exploration–exploitation balance. When applied to the CBIS‑DDSM and MIAS datasets, WPS‑CNN‑SP achieved better results than recent works. In particular, it achieved an Area Under the receiver-operating-characteristic Curve (AUC) = 98.28%, and an Accuracy = 98.99% on CBIS‑DDSM and AUC = 99.76% with Accuracy = 98.99% on MIAS dataset. These findings confirm the potential of WPS as a fast, reliable optimiser for computer‑aided BC screening systems.