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.
Table of Contents:
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
7. Precision Molecular
Imaging Can Perhaps Be Enhanced For Lung Cancer Management Via Integrated Analysis Of General
Parameters Such As Age, Gender, Genetics, And Lifestyle
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
About the Author :
Ayman El-Baz, Ph.D., is is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at AlAlamein International University (UofL-AIU), New Alamein City, Egypt. Dr. El-Baz earned his B.Sc. and M.Sc. degrees in electrical engineering in 1997 and 2001, respectively. He earned his Ph.D. in electrical engineering from the University of Louisville in 2006. Dr. El-Baz was named as a Fellow for Coulter, AIMBE and NAI for his contributions to the field of biomedical translational research. Dr. El-Baz has almost two decades of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or co-authored more than 700 technical articles.
Jasjit S. Suri, PhD, MBA is a Fellow of IEEE, AIMBE, SVM,
AIUM, and APVS. He is currently the Chairman of AtheroPoint,
Roseville, CA, USA, dedicated to imaging technologies for
cardiovascular and stroke. He has nearly ~22,000 citations, co-authored 50 books, and has an H-index of 72.
