Suad Alramouni

A hybrid deep learning framework with fractional multi-scale directional texture features for automated waste classification

Accurate waste image classification is essential for intelligent recycling and sustainable waste management systems. However, reliable waste recognition remains challenging due to irregular object shapes, illumination variations, complex backgrounds, and limited annotated data. A...

A multiplicative additive bias variational framework for accurate and interpretable brain MRI segmentation in cloud-based medical imaging systems

Abstract Accurate brain magnetic resonance imaging (MRI) segmentation remains challenging due to intensity inhomogeneity, acquisition-related bias fields, and ambiguous tissue boundaries. To address these challenges, a Multiplicative–Additive Bias Single-Function Dual-Level-Set (...