Academic Press Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis: Volume 4 (Advances in ubiquitous sensing applications for healthcare, Volume 4)

Product Description Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Review A comprehensive guide to the development of classification techniques for computer-aided diagnosis and medical image processing From the Back Cover Classification in Clinical Applications covers the most current advances in applying classification techniques to a wide variety of clinical applications, appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management, and computer aided diagnosis (CAD) systems design. Classification techniques are used for medical image analysis as a part of computer aided diagnosis systems, which employ machine learning, data analysis, pattern recognition and other deep learning techniques. Classification is an important statistical/mathematical tool used to refine and analyze complex systems such as medical images. The clinical applications can be across a wide range of diseases/procedures/specialties and are included as examples in the book. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN), and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images because they contain tools for data pre-processing, classification, regression, clustering, association rules and visualization. Medical image classification is essential for accurate diagnosis and development of computer aided diagnosis (CAD) systems in clinical applications. For physicians, automated classification of medical images is an increasingly significant tool in their daily activity. Advances in medical imaging technology supported by computer science have enhanced interpretation of medical images and contributed to early diagnosis. The development of CAD systems provides a method of assisting physicians in the detection of abnormalities, quantification of disease progress and alternate diagnosis of lesions. Extraction of appropriate features is a vital step in any image classification. Furthermore, since medical images are often highly textured, texture analysis becomes crucial in medical image analysis. About the Author Nilanjan Dey is 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. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD from Jadavpur University in 2015. He has authored/edited more than 70 books with Elsevier, CRC Press, and SpringerNature, and published more than 300 papers. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence (IGI Global), Associated Editor of IEEE Access, and International Journal of Information Technology (Springer). He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (Springer), Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier), Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Sign