Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Coordinators: Dey Nilanjan, Borra Surekha, Ashour Amira S., Shi Fuqian

Language: English

164.01 €

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345 p. · 19x23.3 cm · Paperback

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.

The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.

1. Ontology-based Process for Unstructured Medical Report Mapping 2. A Computer-aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images 3. A DEFS based System for Differential Diagnosis between Severe Fatty Liver and Cirrhotic Liver using Ultrasound Images 4. Infrared Thermography and Soft Computing for Diabetic Foot Assessment 5. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN and SVM Classifiers using HRV Analysis 6. Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization using Digitized Screen Film Mammograms 7. Optimization of ANN architecture: A review on nature-inspired techniques 8. Ensemble Learning Approach to Motor-Imagery EEG Signal Classification 9. Medical Images Analysis Based on Multi-Label Classification Methods 10. Figure Search in Biomedical Domain: A Survey of Techniques and Challenges 11. Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images 12. Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective

Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, JIS University, Kolkata, India. He is a visiting fellow of the University of Reading, UK, and also holds the position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held the honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He got his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer Nature), Data-Intensive Research (Springer Nature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He is an associate editor of IET Image Processing (Wiley) and an editorial board member of Complex & Intelligent Systems (Springer Nature), Applied Soft Computing (Elsevier), and more. He has written 110 books and over 300 other publications in the areas of medical imaging, machine learning, computer aided diagnosis, data mining, etc. His works have been cited over 15,000 times. He is India’s Ambassador to the International Federation for Information Processing—Young ICT Group and a senior member of IEEE.
Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published 1 edited book, 8 book chapters and 22 research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a
  • Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging
  • Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining
  • Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains