Detection Systems in Lung Cancer and Imaging, Volume 1
Ayman El-Baz (Hrsg.), Jasjit Suri (Hrsg.)
* Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.
Institute of Physics Publishing
Naturwissenschaften, Medizin, Informatik, Technik / Medizin
Beschreibung
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.
Kundenbewertungen
Early diagnosis, Deep Learning, NLP, lung nodule classification, Machine Learning., Ground Glass Opacity (GGO), Ground Glass Diagnosis, Lung Nodule Segmentation, CT Imaging, Fast Caps Net, Lung Nodule Segmentation Framework, Lung cancer, SVM classifier