Twins2-Sat: A Taylor Bird Swarm Optimized Twin-Spatially‎A separable Self-Attention Transformer Model for Highly ‎Accurate Breast Cancer Classification

  • Authors

    • P. S. Anu Rakhi Research Scholar, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
    • R. S. Rajesh Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
    https://doi.org/10.14419/qnnt4s07

    Received date: May 21, 2025

    Accepted date: June 29, 2025

    Published date: August 27, 2025

  • Adaptive Savitzky-Golay Filter (ASGF); Markov Random Field (MRF); GLCM; TwinS²SAT; TBSA

  • Abstract

    In light of the growing need for sophisticated diagnostic systems in medical imaging, this work presents a novel framework for the ‎diagnosis of breast cancer that focuses on accurate tissue characterization using texture and attention-based learning. Mammography images ‎of the breast are first preprocessed using an Adaptive Savitzky-Golay Filter (ASGF) to reduce noise and improve important structures. The ‎next step is the precise segmentation of problematic areas using the Markov Random Field (MRF) model, which efficiently defines tumor ‎borders by utilizing spatial dependencies. Following the isolation of the region of interest, texture features are extracted using the Gray-Level ‎Co-occurrence Matrix (GLCM), which uses statistical metrics like energy, contrast, and correlation to capture crucial spatial correlations in ‎pixel intensities. After that, these descriptive characteristics are sent to a TwinS²SAT model, which is an improved Twins-Spatially ‎Separable Self-Attention (SSSA) Transformer that has been tuned using the Taylor Bird Swarm Algorithm (TBSA) to enhance ‎classification performance and learning efficiency. The combination of a lightweight, attention-driven deep architecture with GLCM features ‎allows for a reliable differentiation between benign and malignant tissue patterns. Python software is used to evaluate the proposed model, and ‎the comparison is made with the Self-Attention Transformer (SAT) model. The results show that compared to SAT with 94.99% of accuracy, ‎the TBSA- TwinS²SAT model ranks with excellent accuracy of 97.94%, precision of 98%, and 99% recall, confirming its potential as a ‎useful tool for automated breast cancer diagnosis‎.

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    Rakhi , P. S. A. ., & Rajesh , R. S. . (2025). Twins2-Sat: A Taylor Bird Swarm Optimized Twin-Spatially‎A separable Self-Attention Transformer Model for Highly ‎Accurate Breast Cancer Classification. International Journal of Basic and Applied Sciences, 14(4), 685-697. https://doi.org/10.14419/qnnt4s07