Detection systems in lung cancer and imaging. Volume 1 / edited by Ayman El-Baz, Jasjit S. Suri. - 1 online resource (various pagings) : illustrations (some color). - [IOP release $release] IOP ebooks. [2021 collection] . - IOP (Series). Release 21. IOP ebooks. 2021 collection. .

"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.




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.

9780750333559 9780750333542

10.1088/978-0-7503-3355-9 doi


Lungs--Cancer--Diagnosis.
Diagnostic imaging--Data processing.
Lung Neoplasms--diagnosis.
Diagnostic Imaging--methods.
Image Processing, Computer-Assisted.
Early Diagnosis.
Medical imaging.
Medical physics and biophysics.

RC280.L8 / D487 2021eb vol. 1

616.99/424075

WF 658 / D479 2021eb vol. 1