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Data Mining in Biomedical Imaging, Signaling, and Systems

2016-04-19 Computers
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Release : 2016-04-19
Genre : Computers
Kind : eBook
Book Rating : 395/5 ( reviews)

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Book Synopsis Data Mining in Biomedical Imaging, Signaling, and Systems by : Sumeet Dua

Download or read book Data Mining in Biomedical Imaging, Signaling, and Systems written by Sumeet Dua. This book was released on 2016-04-19. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems. Written by experts in the field, it presents data mining techniques in the context of various important clinical issues, including diagnosis and grading of depression, identification and classification of arrhythmia and ischemia, and description of classification paradigms for mammograms. The book provides ample information and techniques to benefit researchers, practitioners, and educators of biomedical science and engineering.

Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes

2015-11-03 Medical
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Release : 2015-11-03
Genre : Medical
Kind : eBook
Book Rating : 122/5 ( reviews)

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Book Synopsis Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes by : Karâa, Wahiba Ben Abdessalem

Download or read book Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes written by Karâa, Wahiba Ben Abdessalem. This book was released on 2015-11-03. Available in PDF, EPUB and Kindle. Book excerpt: Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

2018-11-30 Science
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Release : 2018-11-30
Genre : Science
Kind : eBook
Book Rating : 87X/5 ( reviews)

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Book Synopsis Machine Learning in Bio-Signal Analysis and Diagnostic Imaging by : Nilanjan Dey

Download or read book Machine Learning in Bio-Signal Analysis and Diagnostic Imaging written by Nilanjan Dey. This book was released on 2018-11-30. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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

Biomedical Data Mining for Information Retrieval

2021-08-06 Computers
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Release : 2021-08-06
Genre : Computers
Kind : eBook
Book Rating : 266/5 ( reviews)

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Book Synopsis Biomedical Data Mining for Information Retrieval by : Sujata Dash

Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash. This book was released on 2021-08-06. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

High-throughput Image Reconstruction and Analysis

2009 Medical
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Release : 2009
Genre : Medical
Kind : eBook
Book Rating : 961/5 ( reviews)

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Book Synopsis High-throughput Image Reconstruction and Analysis by : A. Ravishankar Rao

Download or read book High-throughput Image Reconstruction and Analysis written by A. Ravishankar Rao. This book was released on 2009. Available in PDF, EPUB and Kindle. Book excerpt: This innovative volume surveys the latest image acquisition advances in serial block face techniques in scanning electron microscopy, knife-edge scanning microscopy, and 4D imaging of multi-component biological systems. The book introduces parallel processing for biological applications. You learn advanced parallelization techniques for decomposing a problem domain and mapping it onto a parallel processing architecture using the message-passing interface (MPI) and OpenMP. Case studies show how these techniques have been successfully used in simulation tasks, data mining, and graphical visualization of biological datasets. You also find coverage of methods for developing scalable biological image databases and for facilitating greater interactive visualization of large image sets.

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