Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. 1. Overview of computer-aided detection model - p. 1 7. 2. Basic terminologies of computed tomography scan - p. 21 8. 3. Terminologies related to lung cancer - p. 67 9. 4. Feature engineering-based methodology for fully automated detection of pulmonary nodules - p. 87 10. 5. Application of convolution neural networks for automated detection of pulmonary nodules - p. 133 11. 6. A fully automated methodology for localization of pulmonary nodules - p. 171 12. 7. Automated risk prediction of solitary pulmonary nodules - p. 199 13. 8. Summary of the book - p. 223 14. Index - p. 237
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. 1. Overview of computer-aided detection model - p. 1 7. 2. Basic terminologies of computed tomography scan - p. 21 8. 3. Terminologies related to lung cancer - p. 67 9. 4. Feature engineering-based methodology for fully automated detection of pulmonary nodules - p. 87 10. 5. Application of convolution neural networks for automated detection of pulmonary nodules - p. 133 11. 6. A fully automated methodology for localization of pulmonary nodules - p. 171 12. 7. Automated risk prediction of solitary pulmonary nodules - p. 199 13. 8. Summary of the book - p. 223 14. Index - p. 237
