Detection systems in lung cancer and imaging.

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Detection systems in lung cancer and imaging.

Institute of Physics (Great Britain), - Personal Name; Suri, Jasjit S., - Personal Name; El-Baz, Ayman S., - Personal Name;

"Version: 202201"--Title page verso.Includes bibliographical references.1. Lung cancer classification using wavelet recurrent neural network / Devi Nurtiyasari, Dedi Rosadi and Abdurakhman -- 2. Diagnosis of diffusion-weighted magnetic resonance imaging (DWI) for lung cancer / Katsuo Usuda and Hidetaka Uramoto -- 3. Computer assisted detection of low/high grade nodule from lung CT scan slices using handcrafted features / Seifedine Kadry and Venkatesan Rajinikanth -- 4. Computer-aided lung cancer screening in computed tomography : state-of the-art and future perspectives / Jo?aao Pedrosa, Guilherme Aresta and Carlos Ferreira -- 5. Radiation therapy in lung cancer treatment / Mary McGunigal, Jonathan W. Lischalk, Pamela Randolph-Jackson and Puja Gaur Khaitan -- 6. Application of visual sensing technology in lung cancer screening / Rongpeng Li, Yonghong Xu and Nana Xu -- 7. Precision molecular imaging can perhaps be enhanced for lung cancer management via integrated analysis of general parameters such as age, gender, genetics, and lifestyle / Kshitish K. Acharya -- 8. Computed tomography ventilation imaging in lung cancer : theory, validation and application / Bilal A. Tahir -- 9. Novel non-invasive methods used in the early detection of lung cancer : from biomarkers to nanosystems / ?Ozlem ?Coban and Ye?sim Kaya Ya?sar -- 10. Heat shock proteins as biomarkers for early-stage diagnosis of lung cancer / Adria Hasan, Suroor Fatima Rizvi, Sana Parveen and Snober S. Mir.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 relating to lung cancer: lung nodule segmentation, lung nodule classification, and Big Data in lung cancer.Academics working in lung cancer, data-mining, machine learning, deep learning and reinforcement learning. Industries working in the areas of healthcare, lung cancer imaging, machine learning, deep learning and reinforcement learning.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at AlAlamein International University (UofL-AIU), New Alamein City, Egypt. Dr. El-Baz earned his B.Sc. and M.Sc. degrees in electrical engineering in 1997 and 2001, respectively. Jasjit S. Suri, PhD, MBA is a Fellow of IEEE, AIMBE, SVM, AIUM, and APVS. He is currently the Chairman of AtheroPoint, Roseville, CA, USA, dedicated to imaging technologies for cardiovascular and stroke. He has nearly ?a22,000 citations, has co-authored 50 books, and has an H-index of 72.Title from PDF title page (viewed on February 11, 2022).

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Series Title: -
Call Number: -
Publisher: : .,
Collation: 1 online resource (various pagings) :illustrations (some color).
Language: English
ISBN/ISSN: 9780750333559
Classification: 616.99/424075
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): Diagnostic Imaging
Medical physics and biophysics.
Medical imaging.
Lungs
Lung Neoplasms
Image Processing, Computer-Assisted.
Early Diagnosis.
Specific Detail Info: -
Statement of Responsibility: edited by Ayman El-Baz, Jasjit S. Suri.

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