
Deep Learning for Medical Applications with Unique Data
- 1st Edition - February 15, 2022
- Editors: Deepak Gupta, Utku Kose, Ashish Khanna, Valentina Emilia Balas
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 1 4 5 - 5
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 1 4 6 - 2
Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real… Read more

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Request a sales quoteDeep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.
- Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets
- Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis
- Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the editors
- Foreword
- Preface
- 1. A deep learning approach for the prediction of heart attacks based on data analysis
- 1. Introduction
- 2. Literature survey
- 3. Materials and method
- 4. Training models
- 5. Data preparation
- 6. Results
- 7. Conclusion
- 8. Note
- 2. A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32: A case of brain tumor
- 1. Introduction
- 2. Literature study
- 3. Discussion and results
- 4. Conclusion
- 3. Deep learning applications for disease diagnosis
- 1. Introduction
- 2. Deep learning
- 3. Methods of evaluation
- 4. Unique data
- 5. Current situation of deep learning in disease diagnosis
- 6. Advantages of deep learning in medical diagnosis
- 7. Applications
- 8. Shortcomings
- 9. Conclusion and future scope
- 4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications
- 1. Introduction
- 2. Cognitive computing concept
- 3. Neural networks concepts
- 4. Metaheuristic algorithm proposal
- 5. Results and discussion
- 6. Future research directions
- 5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture
- 1. Introduction
- 2. Literature review
- 3. Data set and image augmentation
- 4. Convolutional neural network architectures and proposed model
- 5. Results and discussion
- 6. Detecting x-ray images through prediction
- 7. Conclusion and future scope
- 6. Early prediction of heart disease using deep learning approach
- 1. Introduction
- 2. Related study
- 3. Dataset
- 4. Classification techniques and performance analysis
- 5. Conclusion
- 6. Discussion
- 7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system
- 1. Introduction
- 2. Machine learning: Regression models
- 3. Machine learning algorithms
- 4. Deep learning models
- 5. Conclusion
- Appendix 1. Models of FCN-8, FCN-16, and FCN-32
- 8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images
- 1. Introduction
- 2. Related works
- 3. Material and methods
- 4. Experimental results
- 5. Discussion and conclusion
- 9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images
- 1. Introduction
- 2. Related work
- 3. Methodology
- 4. Results and discussion
- 5. Conclusion
- 10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic
- 1. Introduction to artificial intelligence methods employed to tackle the COVID-19 pandemic
- 2. State of the art
- 3. General description of the trade-off model
- 4. Methods adapted to the field of COVID-19–related applications
- 5. Impacts of sanitary measures on the economy
- 6. Conclusion
- 11. Prediction of COVID-19 using machine learning techniques
- 1. Introduction
- 2. Motivation
- 3. Applications of AI, machine learning, and deep learning
- 4. Coronavirus disease-2019 prediction using machine learning
- 5. Conclusion
- Index
- No. of pages: 256
- Language: English
- Edition: 1
- Published: February 15, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128241455
- eBook ISBN: 9780128241462
DG
Deepak Gupta
Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision.
UK
Utku Kose
AK
Ashish Khanna
VB