Alzheimer disease (AD) is a progressive neurodegenerative disorder that impairs memory and cognitive function in older adults, placing a substantial burden on global healthcare systems. Early and accurate diagnosis is crucial for timely intervention, yet manual interpretation of magnetic resonance imaging (MRI) scans is often subjective, time-consuming, and prone to inter-observer variability and bias. To overcome these challenges, we propose NES-Net (Neuro-Synergy Network), a novel deep learning model for automated Alzheimer's detection from MRI data. NES-Net employs a multi-branch hybrid architecture that simultaneously captures structural, spatial, and semantic features of brain images. B... More >

Accurate and automated detection of dental anatomy is essential for diagnosis and treatment planning. This study proposes a Transformer-Embedded YOLOv8 (TEYOLOv8) network to improve detection and classification of seven dental classes. The model enhances localization and segmentation accuracy by embedding an attentive transformer mechanism into the YOLOv8 framework. The TEYOLOv8 architecture integrates data augmentation, feature localization, segmentation, and classification. It employs C2f (Cross-Stage Partial Focus) convolutional blocks to preserve partial segmentation features and introduces a C2fAttn (Cross-Stage Feature Attention) module to capture fine-grained spatial details such as s... More >