Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care
Description
This book explains medical image processing and analysis using deep learning algorithms to analyze medical data. It focuses on the latest achievements and developments in applying this analysis to medical imaging, clinical, and other healthcare applications.
The book covers among other areas:
- Image acquisition and formation.
- Computer-aided diagnosis.
- Image classification.
- Feature extraction.
- Image enhancement/segmentation.
Medical image processing issues such as segmentation, visualization, registration, and navigation may seem to be distinct, yet they are all intertwined in the process of resolving clinical bottlenecks. Using deep learning algorithms, researchers were able to achieve record-breaking performance and set the bar for future research. Due to the extensive quantity of medical imaging data of CT scan, ultrasound, and MRI, there is widespread use of machine learning, specifically deep learning, to discover specific patterns on such data. Such large data is well quantified by deep learning models. Deep learning is now being utilized, customized, and particularly developed for medical image analysis, as opposed to when it was first introduced to the community. Having learned more about the techniques, researchers have come up with innovative ideas for combining artificial intelligence (AI) with neural networks to solve difficult issues like medical image reconstruction.
The key features of this book are:
- Machine learning and deep learning applications.
- Medical imaging applications.
- Feature extraction and analysis.
- Medical image classification, segmentation, recognition, and registration.
- Medical image analysis and enhancement.
- Handling medical image dataset.
More details
Other editions
Additional editions
Persons
Manoj Diwakar is a Professor at Graphic Era Deemed to be University, Dehradun, India. His research interests include image processing, computer vision, medical imaging, and information security. He has authored 250+ papers in reputed journals/conferences (IEEE, Springer, Elsevier) and has been listed among the World's Top 2% Scientists by Stanford University and Elsevier (2023-2024). He serves in various editorial roles across SCI/SCIE/Scopus/ESCI-indexed journals, and has edited books with Springer, CRC, and Bentham Science.
Vinayakumar Ravi is an Assistant Research Professor at the Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia. His current research interests include applications of data mining, artificial intelligence, and machine learning (including deep learning) in biomedical informatics, cybersecurity, image processing, and natural language processing. Dr. Ravi has more than 100 research publications. He organized a shared task on detecting malicious domain names (DMD 2018) as part of SSCC'18 and ICACCI'18. He received the Chancellor's Research Excellence Award at AIRA 2021, and his name was included in the list of the World's Top 2% Scientists by Stanford University, published in
PLOS Biology
.
Prabhishek Singh is an Associate Professor at Bennett University and Adjunct Faculty at Maryam Abacha American University, Nigeria. Recognized as a Top 2% Scientist (2024), his work focuses on image processing, computer vision, deep learning, and machine learning. He has published 210+ papers and serves in various editorial roles across SCI/SCIE/Scopus/ESCI-indexed journals.
Hoang Pham is Distinguished Professor and former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University. Before joining Rutgers in 1993, he was Senior Engineering Specialist with the Idaho National Engineering Laboratory, Idaho Falls, Idaho and Boeing Company in Seattle, Washington. His research areas include reliability modeling and prediction, software reliability, and statistical inference. He is Editor-in-Chief of the International Journal of Reliability, Quality and Safety Engineering and Editor of Springer Series in Reliability Engineering and has been Conference Chair and Program Chair of over 50 international conferences and workshops. Dr. Pham is Author or Co-author of 7 books and has published over 220 journal articles, 100 conference papers, and edited 17 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 50 invited keynote and plenary speeches at many international conferences and institutions. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is Fellow of the IEEE, AAIA, and IISE.
Content
Enhancing dysarthric speech for improved clinical communication: A deep learning approach.- Speech-based real-world scene understanding for assistive care of the visually impaired.- Medical image segmentation with deep learning: An overview.- Lightweight generative model for synthetic biomedical images with enhanced quality.- Pediatric dental disease detection using X-ray image enhancements and deep learning algorithms.- Evaluation of Parkinson disease from MRI images using deep learning techniques.- Analyzing the effect of eyes open and eyes closed states on EEG in Parkinson's disease with ON and OFF medication.- Automated detection of diabetic retinopathy using ResNet-50 deep learning model.- Deep learning model for decoding subcortical brain activity from simultaneous EEG-FMRI multi-model data.- Secure transmission of medical images in IoMT for smart cities using data hiding scheme.- Deep learning approaches to heart stroke prediction: Model evaluation and insights.- Harnessing predictive modeling techniques for early detection and management of diseases: Challenges, innovations, and future directions.- Fundamentals of machine learning and deep learning for healthcare applications.- Automatic detection of Parkinson disease through various machine learning models.- Transforming healthcare: The role of AI and ML in disease prediction, treatment, and patient satisfaction.- Multi-modality medical (CT, MRI, ultrasound etc.) Image fusion using machine learning/deep learning.- Leveraging digital devices for objective behavioral health assessment: Computational machine learning methods for sleep and mental health evaluation.- Optimizing medical image quality through hybrid machine learning techniques and convolutional denoising autoencoders.- Image segmentation in multimodal medical imaging using deep learning models.- Brain MRI analysis for multiple sclerosis detection using deep learning techniques.
