Deep Learning-Based Decision Fusion for Breast Cancer Classification Using Multi-Source Medical Data

Zahaby, Mohammad; Boroumandzadeh, Mostafa; Makhdoom, Iman

doi:10.30473/coam.2025.73974.1295

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	Deep Learning-Based Decision Fusion for Breast Cancer Classification Using Multi-Source Medical Data
Control and Optimization in Applied Mathematics
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 تیر 1404 اصل مقاله (998.34 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.73974.1295
نویسندگان
Mohammad Zahaby^* ¹؛ Mostafa Boroumandzadeh¹؛ Iman Makhdoom²
¹Department of Computer engineering and information technology‎, ‎Payame Noor University‎, Tehran, ‎Iran.
²Department of Statistics‎, ‎Payame Noor University‎, Tehran, ‎Iran‎.
چکیده
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.
تازه های تحقیق
Highlights Developed a novel Medical Decision Support System (MDSS) framework utilizing Yager’s rule of combination to improve breast cancer diagnosis and classification accuracy. Integrated heterogeneous data sources, including Combined Medical Text Reports (MTR), Electronic Health Records (EHR), and medical imaging, to enhance decision-making robustness. Employed Principal Component Analysis (PCA) for effective feature extraction from textual and structured data, followed by classification using CNN, MLP, and SVM models. Applied U-Net architecture for preprocessing and accurate classification of medical images. Developed a weighted Yager decision fusion algorithm that dynamically assigns importance to classifiers based on their accuracy, enhancing BI-RADS categorization. Achieved state-of-the-art performance metrics, including accuracy, sensitivity, specificity, PPV, NPV, and F1-score, outperforming individual classifiers and traditional fusion methods. Demonstrated a maximum overall classification accuracy of 96.23%, surpassing individual classifiers (CNN, MLP, SVM, U-Net) and the original Yager approach. Confirmed that combining multiple classifier outputs significantly enhances the reliability and precision of breast cancer diagnostics in clinical decision support systems.
کلیدواژه‌ها
Medical decision support system‎؛ ‎Text mining‎؛ ‎BI-RADS‎؛ ‎Deep learning

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Deep Learning-Based Decision Fusion for Breast Cancer Classification Using Multi-Source Medical Data