|
Introduction
Health care environment is very much reliant on advance computing. With the advancement in deep neural network, the use of deep learning methods provides better and advance techniques that assists the physician in many cases, rapid identification of diseases and diagnosis in real time. Furthermore, multi-modal imaging enables examining more than one molecule at a time, so that cellular events may be examined simultaneously, or the progression of these events can be followed in real-time. Recent examples of nano-based multi-modal imaging include simultaneous NIR fluorescence imaging, SERS-based imaging. Deep learning techniques are used by various biomedical applications such as Medical Image Registration Using Genetic Algorithm, Machine Learning techniques to solve prognostic problems in medical domain, Artificial Neural Networks in diagnosing cancer and Fuzzy Logic in various diseases. To turn-over the situation, the biomedical market observes a range of research and development activities to integrate biomedical with soft computing. This development provides an expanded growth opportunity for the biomedical technology, given that the technology is set to increase the benefits many folds. Hence, there is a strong need of providing well organized study material with practical aspect and validation. The book smartly fills the gaps.
Recommended topics include, but are not limited to, the following:
Review in the fields of multi-modal imaging, Machine Learning and Medical Data Analysis.
Applications and Practical Systems for Healthcare
Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging
Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
Prediction Models
Mathematical methods for analysis of data collected using ECG, EEG, MRI, X-Ray and CT Scan
Biomedical Image Analysis
New model or improved model of convolutional neural network
Genetic Data Analysis
Visualization and explainable deep neural network.
Multi-modal Imaging for Biomedical Applications.
Edge computing with machine Learning for healthcare
Researchers and practitioners are invited to submit on or before October 14, 2019, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by October 27, 2019 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by - November 27, 2019 and all interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.
Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Deep neural networks for emerging Multimodal Imaging and Bio-Medical Applications. All manuscripts are accepted based on a double-blind peer review editorial process.
All proposals should be submitted through the eEditorial Discovery®TM online submission manager.
Editors
Dr. A. Suresh,
Professor & Head,
Department of Computer Science and Engineering,
Nehru Institute of Engineering and Technology,
T.M.Palayam, Coimbatore-641105, TamilNadu, India
Email: [email protected]
R. Udendhran,
Associate Professor
Department of Computer Science and Engineering,
Bharathidasan University, Trichy- 620024, TamilNadu, India
E-mail: [email protected]
Dr.S. Vimal,
Assistant Professor (Senior Grade),
Department of Information Technology,
National Engineering College,
Kovilpatti, Tamilnadu, India.
Email: [email protected]
|
