Deep Learning for Medical Applications with Unique Data
- 1st Edition - February 15, 2022
- Latest edition
- Editors: Deepak Gupta, Utku Kose, Ashish Khanna, Valentina Emilia Balas
- Language: English
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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Description
Description
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 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.
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
1. A deep learning approach for the prediction of heart attacks based on data analysis
2. A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32: A case of brain tumor
3. Deep learning applications for disease diagnosis
4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications
5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture
6. Early prediction of heart disease using a deep learning approach
7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system
8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images
9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images
10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic
11. Prediction of COVID-19 using machine learning techniques
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 15, 2022
- Language: English
About the editors
About the editors
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
Ashish Khanna is Professor in Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain. His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning.
VB