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This book focuses on major trends and challenges in the detection of lung cancer, presenting work aimed at identifying new techniques and their use in biomedical analysis. This volume covers recent advancements in lung cancer and imaging detection and classification, examining the main applications of Computer aided diagnosis (CAD) relating to lung cancer: lung nodule segmentation, lung nodule classification, and Big Data in lung cancer. Ideal for academics working in lung cancer, data-mining, machine learning, deep learning and reinforcement learning, as well as industry professionals working in the areas of healthcare, lung cancer imaging, machine learning, deep learning and reinforcement learning, this edited collection comprises an essential reference for researchers at the forefront of the field, and provides a high-level entry point for more advanced students.
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.
Contents
1. Lung Cancer Classification Using Wavelet Recurrent Neural Network
2. Diagnosis of Diffusion-Weighted Magnetic Resonance Imaging (DWI) For Lung Cancer
3. Computer Assisted Detection of Low/High Grade Nodule from Lung CT Scan Slices Using Handcrafted Features
4. Computer-Aided Lung Cancer Screening in Computed Tomography: State of the Art and Future Perspectives
5. Radiation Therapy in Lung Cancer Treatment
6. Application of Visual Sensing Technology in Lung Cancer Screening
8. Computed Tomography Ventilation Imaging In Lung Cancer: Theory, Validation And Application
9. Novel Non-invasive Methods Used in the Early Detection of Lung Cancer: From Biomarkers to Nanosystems
10. Heat Shock Proteins as Biomarkers for Early Stage Diagnosis of Lung Cancer