Payame Noor University (PNU) Control and Optimization in Applied Mathematics 2383-3130 10 2 2025 07 01 Deep Learning-Based Decision Fusion for Breast Cancer Classification Using Multi-Source Medical Data 51 73 12052 10.30473/coam.2025.73974.1295 EN Mohammad Zahaby Department of Computer engineering and information technology‎, ‎Payame Noor University‎, Tehran, ‎Iran. Mostafa Boroumandzadeh Department of Computer engineering and information technology‎, ‎Payame Noor University‎, Tehran, ‎Iran. Iman Makhdoom Department of Statistics‎, ‎Payame Noor University‎, Tehran, ‎Iran‎. Journal Article 2025 02 09 Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality‎. ‎Mammography is the primary imaging modality for the early detection of breast tumors‎. ‎Providing timely and highly accurate diagnoses is a top priority for physicians and healthcare providers in the management of critical illnesses‎. ‎This paper presents a Medical Decision Support System (MDSS) that utilizes Yager’s rule of combination to classify and diagnose breast cancer patients by integrating information from multiple data sources‎. ‎Medical text reports (MTR) and key feature vectors extracted from electronic health records (EHR) were reduced using Principal Component Analysis (PCA) and then classified using Convolutional Neural Networks (CNN)‎, ‎Multi-Layer Perceptrons (MLP)‎, ‎and Support Vector Machines (SVM)‎. ‎Medical images were preprocessed and classified using a U-Net model‎. ‎A novel decision fusion algorithm‎, ‎called weighted Yager‎, ‎was introduced to determine the Breast Imaging-Reporting and Data System (BI-RADS) categories‎, ‎taking into account the accuracy of each class in each classifier as evidence‎. ‎The performance of the proposed system was evaluated based on standard metrics including accuracy‎, ‎sensitivity‎, ‎specificity‎, ‎positive predictive value (PPV)‎, ‎negative predictive value (NPV)‎, ‎and F1-score‎. ‎The proposed system achieved the highest accuracy of 96.23\%‎, ‎outperforming individual classifiers (CNN‎: ‎86.37%‎, ‎MLP‎: ‎92.11%‎, ‎SVM‎: ‎87.92%‎, ‎U-Net‎: ‎92.97%‎, ‎and Yager‎: ‎93.49%)‎. ‎The weighted Yager fusion method yielded the best performance with an accuracy of 96.23%‎, ‎sensitivity of 98.80%‎, ‎specificity of 85.90%‎, ‎PPV of 86.21%‎, ‎NPV of 97.82%‎, ‎and F1-score of 85.87%‎. ‎These findings demonstrate that integrating decisions from multiple classifiers significantly improves diagnostic accuracy and robustness‎. https://mathco.journals.pnu.ac.ir/article_12052_63faa299c3258fda2030420bd4cc7537.pdf