Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Author :
Publisher : Academic Press
Total Pages : 348
Release :
ISBN-10 : 9780128160879
ISBN-13 : 012816087X
Rating : 4/5 (79 Downloads)

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 and published by Academic Press. This book was released on 2018-11-30 with total page 348 pages. 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


Machine Learning in Bio-Signal Analysis and Diagnostic Imaging Related Books

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
Language: en
Pages: 348
Authors: Nilanjan Dey
Categories: Science
Type: BOOK - Published: 2018-11-30 - Publisher: Academic Press

DOWNLOAD EBOOK

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical s
Classification and Clustering in Biomedical Signal Processing
Language: en
Pages: 502
Authors: Dey, Nilanjan
Categories: Technology & Engineering
Type: BOOK - Published: 2016-04-07 - Publisher: IGI Global

DOWNLOAD EBOOK

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imagi
Medical Imaging
Language: en
Pages: 251
Authors: K.C. Santosh
Categories: Computers
Type: BOOK - Published: 2019-08-20 - Publisher: CRC Press

DOWNLOAD EBOOK

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly title
Handbook of Deep Learning in Biomedical Engineering
Language: en
Pages: 322
Authors: Valentina Emilia Balas
Categories: Science
Type: BOOK - Published: 2020-11-12 - Publisher: Academic Press

DOWNLOAD EBOOK

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large
Deep Learning in Medical Image Analysis
Language: en
Pages: 184
Authors: Gobert Lee
Categories: Medical
Type: BOOK - Published: 2020-02-06 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, imag