Modified YOLOv8x model for coronary stenosis detection and troponin risk stratification
Published in: Discover computing
Jan 20, 2026
Detection of coronary artery stenosis and risk stratification of troponin play a pivotal role in offering early diagnosis and treatment of cardiovascular diseases. In this paper, an improved deep learning framework that allows using both spatial and frequency-based attention mechanisms will be proposed using a modified YOLOv8x framework. Upon benchmarking YOLOv8, YOLOv9, and YOLOv10 models, YOLOv8x was chosen due to its excellent baseline, and the enhancement was done to make it more clinically...
Federated Generative Models in Medical Imaging: Current Advances, Challenges, and Future Directions
Published in: IEEE Access
Jan 05, 2026
Haroon Mahmood
Zareen Alamgir
Sobia Tariq Javed
Saira Karim
Muhammad Awais
The fusion of Federated Learning (FL) and deep generative models is transforming medical imaging by enabling privacy-preserving and data-efficient machine learning. Training large-scale deep models on radiological imaging data remains challenging due to data scarcity, heterogeneity, and strict privacy constraints that limit data sharing across institutions. FL addresses these challenges by employing collaborative model training that does not expose raw patient data, while generative models synt...
An optimized SDN framework for the internet of things
Published in: Discover Computing
Jan 01, 2026
Zina houhamdi
mohamed Raid Athamena
Belkacem Athamena
Shorouq F. Eletter
Low-power wireless networks (LPWN) have traditionally been central to the Internet of Things (IoT) discussion. Nevertheless, as these networks grow more complex, their control architectures and protocols reveal significant limitations, particularly when dealing with multi–hop topologies and lossy channels. To tackle these challenges, there has been growing interest in adopting Software–Defined Networking (SDN), which has revolutionized data center and campus network management over the past dec...