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

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 F۱-score‎. ‎The proposed system achieved the highest accuracy of ۹۶.۲۳\%‎, ‎outperforming individual classifiers (CNN‎: ‎۸۶.۳۷%‎, ‎MLP‎: ‎۹۲.۱۱%‎, ‎SVM‎: ‎۸۷.۹۲%‎, ‎U-Net‎: ‎۹۲.۹۷%‎, ‎and Yager‎: ‎۹۳.۴۹%)‎. ‎The weighted Yager fusion method yielded the best performance with an accuracy of ۹۶.۲۳%‎, ‎sensitivity of ۹۸.۸۰%‎, ‎specificity of ۸۵.۹۰%‎, ‎PPV of ۸۶.۲۱%‎, ‎NPV of ۹۷.۸۲%‎, ‎and F۱-score of ۸۵.۸۷%‎. ‎These findings demonstrate that integrating decisions from multiple classifiers significantly improves diagnostic accuracy and robustness‎.

Keywords:

Medical decision support system‎ , ‎Text mining‎ , ‎BI-RADS‎ , ‎Deep learning