Deep Learning-Based Lung Cancer Segmentation and Volumetric Analysis Using CT Imaging: A Comprehensive Survey.

Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model

This study concludes that integrating VGG16 and U-Net+ into this CNN architecture can significantly enhance lung cancer detection performance by achieving a remarkable accuracy of 96% and provide a reliable tool for clinicians in early stage diagnosis and treatment monitoring.

Multi-class lung cancer detection and classification from CT scans using CNN transformer

AI-Driven Tumor Characterization and Histological Subtype Classification in Lung Cancer Using CT Imaging

The proposed framework effectively incorporated MedSAM-based tumor segmentation, radiomic feature analysis, and deep feature representation and sequential dependency modeling all in a single diagnostic workflow for lung cancer evaluation and diagnosis.

A new approach for lung cancer diagnosis: parallel feature learning

A parallel feature learning architecture developed for lung CT images was designed, which focuses on learning different features from each parallel path by using deformable and dilated convolution layers together and offers a reliable solution for lung cancer diagnosis.

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

The proposed system demonstrates potential as an assistive tool for automated lung cancer screening, warranting further validation on larger and multi-institutional datasets before clinical application.

First steps in clinical implementation of a lung nodule classification method by artificial intelligence

This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.