COVID-19 Radiological Lung Imaging

Autorentext
Luca Saba is Dean of School of Medicine at the University of Cagliari, full professor of Radiology, and Chief of the Department of Radiology in the A.O.U. of Cagliari. His research is focused on Multi-Detector-Row Computed Tomography, Magnetic Resonance, Ultrasound, Neuroradiology, and Diagnostic in Vascular Sciences. Dr Saba has written 21 book-chapters, authored more than 370 per-reviewed papers, and served as editor on 14 books in the field of Computed Tomography, Cardiovascular, Plastic Surgery, Gynecological Imaging and Neurodegenerative imaging. Dr. Saba is a frequently invited guest speaker and has presented more than 500 papers and posters in National and International Congress (RSNA, ESGAR, ECR, ISR, AOCR, AINR, JRS, SIRM, AINR). Dr. Saba has won 15 scientific and extracurricular awards during his career. Sushant Agarwal, BE, is currently a software engineer with Tech-Net Inc and has experience with working on edge devices such as Raspberry Pie, NVIDIA Jetson Nano, and Intel Neural compute stick. He is passionate about deep learning and has a good command of Python, SQL, Deep Learning, and Computer Vision. Sushant Agarwal holds Udacity Nanodegrees in Deep Learning and Data Science as well as a Specialization in Artificial Intelligence for Medical Imaging and has been a top performer in various national and international competitions. He has published over 11 research papers internationally in the field of computer vision and deep learning.Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General's Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.

Klappentext

COVID-19 Radiological Lung Imaging: A Classic Artificial Intelligence Framework introduces modern AI technologies for early detection of COVID-19 disease to assist in saving patient lives and safeguarding frontline workers. With a strong focus on Deep Learning, the book examines specific detection and classification techniques in lung X-ray imaging, computed tomography lung imaging, deep learning on edge devises and bias measurements, deep learning for cloud and explainable AI for validation, and discusses the medical impact and AI implications for COVID-19 in lung pathologies. It is therefore an ideal reference for researchers and clinicians working in radiology and pulmonary medicine to learn the modern AI technologies in COVID-19 paradigms for implementation.

Inhalt

Section 1: X-Ray Lung Imaging using Deep Learning

1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series

Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning

5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation

Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging

9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques

Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning

12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients

Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation

15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment

Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies

17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.