Application of Artificial Intelligence in Early Detection of Lung Cancer
- 1st Edition - May 10, 2024
- Imprint: Academic Press
- Authors: Madhuchanda Kar, Jhilam Mukherjee, Amlan Chakrabarti, Sayan Das
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 2 4 5 - 3
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 2 4 6 - 0
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose… Read more
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Request a sales quoteApplication of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling.
This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.
- Provides an overview of the latest developments of artificial intelligence technologies applied to the detection of pulmonary nodules
- Discusses the different technologies available and guides readers step-by-step to the most applicable one for the specific lung cancer type
- Describes the entire study design on prediction of lung cancer to help readers apply it to their research successfully
Researchers and graduate students on cancer research, oncologists, Clinical researchers, radiologists
- Cover image
- Title page
- Table of Contents
- Copyright
- 1. Overview of computer-aided detection model
- 1.1. Computer-aided detection and diagnosis
- 2. Basic terminologies of computed tomography scan
- 2.1. Introduction
- 2.2. Basic terminologies
- 2.3. Generations of CT scanner machines
- 2.4. CT scanning technology
- 2.5. Reconstruction
- 2.6. Cone-beam geometry versus parallel fan-beams geometry
- 2.7. Single-slice CT
- 2.8. Image quality
- 2.9. Projections on CT imaging
- 2.10. Digital Imaging and Communications in Medicine
- 3. Terminologies related to lung cancer
- 3.1. Introduction
- 3.2. Pulmonary abnormalities detectable on CT scan images
- 3.3. Pulmonary abnormalities that create accurate detection of pulmonary nodules
- 3.4. Pulmonary cyst
- 3.5. Pulmonary fibrosis
- 3.6. Consolidation
- 3.7. Types of nodules based on density
- 3.8. Types of pulmonary nodules based on anatomical positions
- 3.9. Morphologies of pulmonary nodules
- 3.10. The margin of pulmonary nodule
- 3.11. Shape of pulmonary nodule
- 4. Feature engineering-based methodology for fully automated detection of pulmonary nodules
- 4.1. Introduction
- 4.2. Pulmonary lesion segmentation
- 4.3. Feature extraction
- 4.4. Object recognition
- 4.5. State of the art lung nodule detection methodology designed using feature engineering methodology
- 5. Application of convolution neural networks for automated detection of pulmonary nodules
- 5.1. Introduction
- 5.2. Introduction to convolutional neural network
- 5.3. Pulmonary nodule detection
- 6. A fully automated methodology for localization of pulmonary nodules
- 6.1. Introduction
- 6.2. Fissure completeness measurement
- 6.3. Pulmonary fissure segmentation
- 7. Automated risk prediction of solitary pulmonary nodules
- 7.1. Introduction
- 7.2. Explainable AI
- 7.3. Saliency maps
- 7.4. Gradient-weighted class activation mapping
- 7.5. Evaluation metrics for explainable AI technique
- 8. Summary of the book
- 8.1. Recap of main topics of the book
- 8.2. Summary of finding or insights
- 8.3. Case studies
- 8.4. Critical discussions
- 8.5. Conclusion
- 8.6. Future directions of this research area
- Index
- Edition: 1
- Published: May 10, 2024
- No. of pages (Paperback): 254
- No. of pages (eBook): 450
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780323952453
- eBook ISBN: 9780323952460
MK
Madhuchanda Kar
Dr. Madhuchanda Kar received her MD (Internal Medicine) and PhD (Cancer Research) degrees from University of Calcutta. She has almost 30 years of experience in medical education and research. In her career, nearly 80 articles have been published in different peer-reviewed medical and scientific journals. She is the recipient of various fellowship, e.g., Fellow of Indian College of Physicians, IMA Institute of Medical Sciences, Indian medical association, and Indian Association of Clinical Medicine. She was the chairperson of Board of Governors of Indian Institute of Science Education and Research. Presently, she is member of Board of Governors of Central University of Gaya. Her research interests have been focused on solid tumors, hematological malignancies, and interdisciplinary research linking cancer diagnosis and technology.
Affiliations and expertise
Clinical Director of Department of Oncology, Peerless Hospital Kolkata, IndiaJM
Jhilam Mukherjee
Jhilam Mukherjee PhD is currently a Senior Research Fellow of Centre of Excellence in Systems Biology and Biomedical Engineering, University of Calcutta, India. She received MCA from West Bengal University of Technology. She has completed her PhD in lung cancer detection using CT images from University of Calcutta and has more than five years of experience on computer-aided diagnosis for lung cancer. Her research interest on medical image processing, computer vision, and data analytics.
Affiliations and expertise
Senior Research Fellow, Centre of Excellence in Systems Biology and Biomedical Engineering, University of Calcutta, IndiaAC
Amlan Chakrabarti
Dr. Amlan Chakrabarti PhD is former Dean, Faculty of Engineering and Technology of University of Calcutta. He was a Post-Doctoral fellow at the School of Engineering, Princeton University, USA, and has almost 20 years of experience in education and research. He is the recipient of Indian National Science Academy Visiting Faculty Fellowship (2014), JSPS Invitation Research Award (2016), Erasmus Mundus Leaders Award (2017), Hamied Visiting Professorship from University of Cambridge (2018) and Siksha Ratna Award by Dept. of Higher Education Govt. of West Bengal (2018). He is Associate Editor of the Elsevier’s Journal of Computers and Electrical Engineering and holds 4 patents. His areas of research are Machine Learning, Computer Vision, Reconfigurable Computing, Cyber-physical Systems, VLSI CAD, and Quantum Computing.
Affiliations and expertise
Full Professor and Director, A.K.Choudhury School of Information Technology, University of Calcutta, IndiaSD
Sayan Das
Sayan Das MD is associate consultant in the Department of Interventional Radiology and Imaging, Peerless Hospital Kolkata. He has received his MD degree from Kathmandu University and has published several research papers on the characteristics of radiological imaging. His research interests are on interventional radiology and imaging and CAD methodologies using different modalities of radiological images.
Affiliations and expertise
Radiation Oncologist and Associate Consultant, Department of Interventional Radiology and Imaging, Peerless Hospital Kolkata, IndiaRead Application of Artificial Intelligence in Early Detection of Lung Cancer on ScienceDirect