Lung Cancer and Imaging

Focuses on the development of automated, non-invasive diagnostic tools for the early detection and classification of pulmonary nodules. Edited by Ayman El-Baz and Jasjit S. Suri, the work explores cutting-edge machine learning algorithms, including 3D convolutional neural networks and capsule networks, to distinguish between benign and malignant tumors. The primary source material details a specific framework that integrates seventh-order Markov–Gibbs random field models to analyze spatial inhomogeneities within CT scans. Beyond imaging, the text addresses epidemiology, biomarkers, and innovative therapies such as cold atmospheric plasma and electromagnetic lung ablation. Ultimately, the collection seeks to improve patient survival rates by providing clinicians with high-accuracy, computer-aided systems that reduce the need for risky surgical biopsies.Get the Book now from Amazon:https://www.amazon.com/Lung-Cancer-Imaging-Expanding-Physics/dp/0750325380?&linkCode=ll2&tag=cvthunderx-20&linkId=54af652ad88f3f960e4f0d66757c3867&language=en_US&ref_=as_li_ss_tlYou can listen and download our episodes for free on more than 10 different platforms:https://linktr.ee/medicine_made_simpleProduced by:https://www.podcaistudio.com/