Applications of Artificial Intelligence in Medical Imaging
- 1st Edition - November 10, 2022
- Imprint: Academic Press
- Editor: Abdulhamit Subasi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 5 0 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 4 5 1 - 2
Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorpora… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteApplications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions.
This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis.
- Discusses new deep learning algorithms for image analysis and how they are used for medical images
- Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system
- Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes
Researchers, professionals, academicians, and graduate students on medical informatics and computer science
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Series preface
- Preface
- Acknowledgments
- Chapter 1. Introduction to artificial intelligence techniques for medical image analysis
- Abstract
- Outline
- 1.1 Introduction
- 1.2 Artificial intelligence for image classification
- 1.3 Unsupervised learning (clustering)
- 1.4 Supervised learning
- References
- Chapter 2. Lung cancer detection from histopathological lung tissue images using deep learning
- Abstract
- Outline
- 2.1 Introduction
- 2.2 Literature review
- 2.3 Artificial intelligence models
- 2.4 Lung cancer detection using artificial intelligence
- 2.5 Discussion
- 2.6 Conclusion
- References
- Chapter 3. Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques
- Abstract
- Outline
- 3.1 Introduction
- 3.2 Literature survey
- 3.3 Deep learning for disease detection
- 3.4 Disease detection using artificial intelligence
- 3.5 Discussion
- 3.6 Conclusion
- References
- Chapter 4. Breast cancer detection from mammograms using artificial intelligence
- Abstract
- Outline
- 4.1 Introduction
- 4.2 Background and literature review
- 4.3 Artificial intelligence techniques
- 4.4 Breast cancer detection using artificial intelligence
- 4.5 Discussion
- 4.6 Conclusion
- References
- Chapter 5. Breast tumor detection in ultrasound images using artificial intelligence
- Abstract
- Outline
- 5.1 Introduction
- 5.2 Background/literature review
- 5.3 Artificial intelligence techniques
- 5.4 Breast tumor detection using artificial intelligence
- 5.5 Discussion
- 5.6 Conclusion
- References
- Chapter 6. Artificial intelligence-based skin cancer diagnosis
- Abstract
- Outline
- 6.1 Introduction
- 6.2 Literature review
- 6.3 Machine learning techniques
- 6.4 Results and discussions
- 6.5 Conclusion
- References
- Chapter 7. Brain stroke detection from computed tomography images using deep learning algorithms
- Abstract
- Outline
- 7.1 Introduction
- 7.2 Literature survey in brain stroke detection
- 7.3 Deep learning methods
- 7.4 Experimental results
- 7.5 Conclusion
- References
- Chapter 8. A deep learning approach for COVID-19 detection from computed tomography scans
- Abstract
- Outline
- 8.1 Introduction
- 8.2 Literature review
- 8.3 Subjects and data acquisition
- 8.4 Proposed architecture and transfer learning
- 8.5 COVID-19 detection with deep feature extraction
- 8.6 Results and discussions
- 8.7 Conclusion
- References
- Chapter 9. Detection and classification of Diabetic Retinopathy Lesions using deep learning
- Abstract
- Outline
- 9.1 Introduction
- 9.2 Literature survey on diabetic retinopathy detection
- 9.3 Deep learning methods for diabetic retinopathy detection
- 9.4 Diabetic retinopathy detection using deep learning
- 9.5 Discussion
- 9.6 Conclusion
- References
- Further reading
- Chapter 10. Automated detection of colon cancer using deep learning
- Abstract
- Outline
- 10.1 Introduction
- 10.2 Literature review
- 10.3 Artificial intelligence for colon cancer detection
- 10.4 Disease detection using artificial intelligence
- 10.5 Discussion
- 10.6 Conclusion
- References
- Chapter 11. Brain hemorrhage detection using computed tomography images and deep learning
- Abstract
- Outline
- 11.1 Introduction
- 11.2 Literature survey in brain hemorrhage detection
- 11.3 Deep learning methods
- 11.4 Experimental results
- 11.5 Discussions
- 11.6 Conclusion
- References
- Chapter 12. Artificial intelligence-based retinal disease classification using optical coherence tomography images
- Abstract
- Outline
- 12.1 Introduction
- 12.2 Related work
- 12.3 Dataset
- 12.4 Implementation details
- 12.5 Results and discussions
- 12.6 Discussion
- 12.7 Conclusion
- References
- Chapter 13. Diagnosis of breast cancer from histopathological images with deep learning architectures
- Abstract
- Outline
- 13.1 Introduction
- 13.2 Materials and methods
- 13.3 Results and discussions
- 13.4 Conclusion
- References
- Chapter 14. Artificial intelligence based Alzheimer’s disease detection using deep feature extraction
- Abstract
- Outline
- 14.1 Introduction
- 14.2 Background/literature review
- 14.3 Artificial intelligence models
- 14.4 Alzheimer’s disease detection using artificial intelligence
- 14.5 Discussion
- 14.6 Conclusion
- References
- Index
- Edition: 1
- Published: November 10, 2022
- No. of pages (Paperback): 380
- No. of pages (eBook): 380
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443184505
- eBook ISBN: 9780443184512
AS
Abdulhamit Subasi
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
Affiliations and expertise
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi ArabiaRead Applications of Artificial Intelligence in Medical Imaging on ScienceDirect