Lung cancer is the second most common cancer in the world, making the development of accurate and fast diagnostic tools a research area of paramount importance.

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. Emphasising the methodological benefits of non-invasive approaches to the diagnosis and classification of lung cancers, this book explores the crucial need to develop a non-invasive diagnostic tool that eliminates the risks associated with the surgical procedure.

Collecting together the work of several significant research teams in the field, 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.