Breast cancer diagnosis using medical imaging remains a critical challenge due to variability in tumor appearance and limitations of single-model prediction systems. While deep learning approaches have yielded encouraging results, concerns with model generalization, robustness, and clinical reliability remain. This study provides a hybrid ensemble deep learning framework for ultrasound-based breast cancer classification, which combines a proprietary Convolutional Neural Network (CNN) with transfer learning models such as VGG16, DenseNet121, and InceptionV3. A total dataset of 5,400 ultrasound images was used. Parameter optimization and preprocessing techniques such as normalization, resizing, and data augmentation were employed. The proposed system uses voting ensemble approaches to combine predictions from individual models. DenseNet121 achieved the highest standalone accuracy (98.2%), whereas the proposed ensemble model demonstrated superior generalization, with an overall accuracy of 96.0%, precision of 96.1%, recall of 96.0%, and F1-score of 95.9%. Soft voting outperformed hard voting in terms of stability and balanced classification performance, whereas hard voting reduced model bias. The data show that ensemble learning increases diagnostic consistency and reliability. The proposed method offers a scalable and robust solution for computer-aided diagnosis systems, with high potential for real-world clinical integration.

This bibliometric survey examines 200 highly cited publications spanning from 2009 to 2025, retrieved from the Scopus database using search terms targeting neuromorphic computing and its applications in healthcare engineering and biomedical systems. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of materials science, device physics, circuit design, computer architecture, and biomedical engineering. Key findings indicate that memristive devices, particularly those based on metal oxides and two-dimensional materials, constitute the dominant hardware platform for neuromorphic computing, with phase-change materials, spintronic devices, and ferroelectric tunnel junctions representing significant alternative approaches. The United States, China, Germany, South Korea, and Switzerland emerge as the most productive nations, with extensive international collaboration networks reflecting the field's multidisciplinary nature. Thematic clustering identifies five major research domains: (1) memristive devices and resistive switching mechanisms, (2) two-dimensional materials for neuromorphic applications, (3) spiking neural networks and neuromorphic architectures, (4) optical and photonic neuromorphic computing, and (5) biomedical and healthcare applications including electronic skin, neural prosthetics, and biosensing. Emerging trends include the integration of neuromorphic computing with edge artificial intelligence for wearable healthcare devices, the development of reservoir computing for real-time biomedical signal processing, and the convergence of neuromorphic systems with biohybrid interfaces. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including device variability, scalability, and biocompatibility, and proposes future directions for healthcare-oriented neuromorphic engineering.

This bibliometric survey examines 200 highly cited publications spanning from 2018 to 2025, retrieved from the Scopus database using search terms targeting federated learning applications in healthcare, medical imaging, and clinical diagnostics. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of distributed machine learning, privacy-preserving technologies, and biomedical informatics. Key findings indicate that federated learning has emerged as a transformative paradigm for collaborative AI model training across healthcare institutions without compromising patient data privacy. The United States, China, the United Kingdom, Germany, and India emerge as the most productive nations, with extensive international collaboration networks reflecting the global need for privacy-compliant healthcare AI. Thematic clustering identifies five major research domains: (1) federated learning architectures and algorithms for healthcare, (2) privacy-preserving techniques including differential privacy, homomorphic encryption, and blockchain integration, (3) medical imaging applications (radiology, pathology, dermatology, ophthalmology), (4) electronic health records and predictive analytics, and (5) wearable devices and remote patient monitoring. Emerging trends include the integration of federated learning with explainable AI for clinical interpretability, the convergence of federated learning with digital twins and the healthcare metaverse, and the development of personalized federated learning frameworks for handling non-IID medical data. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including data heterogeneity, communication efficiency, and security vulnerabilities, and proposes future directions for privacy-preserving healthcare AI.

Digital twin technology, which creates virtual replicas of physical entities, has emerged as a transformative paradigm in healthcare engineering, enabling personalized medicine, surgical simulation, and real-time patient monitoring. This study presents a comprehensive scien-tometric analysis of 200 highly cited articles retrieved from Scopus using a targeted search string focused on digital twins, virtual patients, and patient avatars in healthcare. The analysis characterizes publication trends, citation impact, leading contributors, thematic clusters, and collaborative networks. Results indicate that research output has accelerated dramatically since 2019, with 2021–2024 representing the most productive period. The highest citation concentrations are found in studies applying digital twins to cardiology, oncology, surgical planning, and healthcare system optimization. The United States, China, Germany, and the United Kingdom dominate production, with strong interdisciplinary collaborations among en-gineering, computer science, and clinical departments. Co-occurrence analysis of author keywords reveals six major thematic clusters: cardiovascular digital twins, surgical simulation and virtual patients, personalized and precision medicine, enabling technologies (IoT, AI, blockchain), healthcare system digital twins, and ethical and regulatory considerations. This scientometric mapping provides researchers, clinicians, and policymakers with an evidence-based roadmap of a rapidly evolving field positioned at the intersection of engineering and medicine.

Early identification of individuals at high risk of stroke is essential for improving prognosis, guiding preventive strategies, and reducing the burden of stroke-related disability. This study presents a comparative machine learning approach for stroke risk prediction using structured healthcare data containing demographic, lifestyle, and clinical variables. The proposed approach evaluates five supervised classification models, namely Random Forest, Support Vector Machine, XGBoost, LightGBM, and CatBoost, to identify the most suitable model for stroke risk classification. A systematic preprocessing pipeline was applied, including median imputation for missing BMI values, categorical feature encoding, stratified data splitting, feature scaling, and Synthetic Minority Over-sampling Technique (SMOTE) to address severe class imbalance in the training data. Model performance was assessed using accuracy, precision, recall, and F1-score Among the evaluated models, CastBoost achieved the most balanced overall performance, while SVM showed higher sensitivity in detecting stroke cases. These findings indicate that model selection should depend on the intended clinical purpose, particularly whether the goal is balanced prediction or improved detection of possible stroke cases. Overall, this study highlights the potential of machine learning to support early stroke risk assessment and preventive healthcare decision-making using routinely available structured health data.