Hossein Najaf-Zadeh

Structure-aware feature-to-image encoding with convolutional neural networks improves peer-to-peer credit default prediction compared with conventional tabular models

Abstract Background Peer-to-peer (P2P) lending has expanded rapidly, exposing individual investors to credit default risk typically absorbed by traditional bank intermediation. Methods This study proposes a hybrid deep learning framework for credit default prediction using 1,335,...

Automated lung tumor segmentation using enhanced U-Net architectures: a multi-backbone comparative study for clinical applications

Lung cancer remains a leading cause of cancer-related mortality worldwide, and manual delineation of tumors on computed tomography (CT) is time-consuming and prone to inter-observer variability. This study systematically evaluates nine deep learning configurations for automated l...

Multi-Backbone U-Net segmentation and interpretable hybrid deep learning classification for lung cancer detection in chest CT images

Lung cancer remains the leading cause of cancer-related mortality worldwide, and its early and accurate diagnosis from chest CT imaging is complicated by overlapping radiological features between benign and malignant lesions, as well as by the limited cross-institution validation...

High-accuracy lung region segmentation and injury classification using deep networks

This study developed and evaluated a hybrid deep learning framework for automated lung segmentation and multi-class severity classification in chest CT scans, addressing key methodological gaps including heterogeneous dataset labeling, pseudo-label bias in segmentation, limited i...