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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging (Paperback)
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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging (Paperback)
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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.
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