eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-12-30 18 4 2799 2816 10.13005/bpj/3295 68693 article Vinoth Rathinam 1 Sasireka Rajendran 2 Valarmathi Krishnasamy 1 Vimala Mannarsamy 1 Department of Electronics and Communication Engineering, P.S.R. Engineering College, Sivakasi, Tamilnadu, India. Department of Biotechnology, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India Medical imaging technologies play a vital role in diagnosing and identifying breast cancer, where initial and accurate detection is essential to improve patient outcomes. However, existing methods face challenges in noise reduction, effective segmentation, and precise classification. This study proposes a novel framework, Breast cancer image classification is performed using a Theory-Guided Convolutional Neural Network (TCNN) enhanced through optimization with the Siberian Tiger Optimization (STO) algorithm, forming the proposed BCI-TCNN-STO model. Input images from the MIAS dataset are pre-processed using the Time-domain Real-valued Generalized Wiener Filter (TRG-WF) for noise reduction, segmented with Localized Sparse Incomplete Multi-View Clustering (LSIMC) to extract Regions of Interest, and classified using a Theory-guided CNN enhanced with STO for improved precision. Experimental evaluation demonstrates that the proposed method consistently outperforms existing techniques in terms of accuracy and error rates. These results highlight the potential of BCI-TCNN-STO as an effective tool to aid radiologists in the early diagnosis of breast cancer and clinical decision-making. https://biomedpharmajournal.org/vol18no4/an-optimized-theory-guided-cnn-framework-with-siberian-tiger-algorithm-for-breast-cancer-image-analysis/ Breast Cancer Incomplete Multi-View Clustering Localized Sparse Time-Domain Real-valued Generalized Wiener Filter Siberian Tiger Optimization Theory-guided Convolutional Neural Network