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This book covers recent advancements in lung cancer and imaging, covering detection and classification. This volume examines Lung Nodule Detection, Lung Nodule and Features Extraction, and Modern Approaches for Lung Nodule Diagnosis. Nodule detection focuses on algorithmic and deep learning practises, and their related CT imaging and dynamic programming methods. The classification applications of texture and shape analysis for nodules are covered, including computerised methods for more effective detection. Finally, the book examines the modern approaches used for nodule diagnosis. Specific focus is placed on deep learning for automated classification, machine learning for data analysis, and CT automatic detection of nodules.
Key Features:
- Unique focus on advance work in detection system and classification systems.
- An updated reference for lung cancer detection via imaging.
- Focus on progressive deep learning and machine learning applications for more effective detection.
Preface
Acknowledgments
Editor biographies
List of contributors
1 A deep ensemble model for the detection and classification of lung cancer in clinical images
2 Segmentation and classification of lung nodule images from the LIDC-IDRI database using a massive-training artificial neural network (MTANN)
3 Predicting cancer survival time from a small CT database using handcrafted features and deep learning
4 Advanced AI model for lung cancer detection and genetic mutation prediction
5 Recent progress in imaging techniques for early lung cancer diagnosis
6 Role of imaging techniques in the screening and detection of lung cancer
7 Deep learning and classical segmentation techniques for lung cancer imaging
8 Deep insights into pulmonary oncology: modern algorithms for robust lung cancer diagnosis
9 Toward explainable AI in lung cancer care: methods, clinical integration, and future cross-cancer insights
10 Virtual biopsies and beyond: integrating AI into precision pulmonology