eBooks
Application of Artificial Intelligence in Early Detection of Lung Cancer by
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.
ISBN: 9780323952453Publication Date: 2024-05-10
Developing the Digital Lung: From Lung CT to Clinical AI by
Reflecting recent major advances in the field of artificial intelligence, Developing the Digital Lung, From First Lung CT to Clinical AI, by Dr. John Newell, is your go-to reference for all aspects of applied artificial intelligence in lung disease development, including application to clinical medicine. It provides a unique overview of the field, beginning with a review of the origins of artificial intelligence in the mid-1970s and progressing to its application to clinical medicine in the early 2020s. Organized based on the four stages of development, this practical, easy-to-use resource helps you effectively apply artificial intelligences to lung imaging. Traces the development of precise quantitative CT of diffuse lung disease through the use of applied AI, leading to faster effective diagnosis of patients with lung disease. Reviews CT manufacturers, models and scanning protocol used to produce the 3D digital maps of the lungs. Discusses how the data processed by AI algorithms can produce measures of emphysema, air trapping, and airway wall thickening in subjects with COPD and measures of pulmonary fibrosis and traction bronchiectasis in idiopathic pulmonary fibrosis (IPF). Demonstrates the differences between reactive machine AI and limited memory AI methods. Includes comprehensive case studies and current information on cloud computing. An eBook version is included with purchase. The eBook allows you to access all of the text, figures and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud.
