Deep learning biomedical signal (31 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: Brand New. 256 pages. 6.14x0.62x9.21 inches. In Stock.

Condición: New. Ngangbam Herojit Singh is presently working as an Assistant Professor at NIT Agartala in the Department of Computer Science and Engineering. He received his Ph.D. degree from the NIT Manipur, and M.Tech and B.Tech from Anna University, Chennai. Hi.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Condición: New.

Taschenbuch. Condición: Neu. Neuware - This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.…

Condición: New.

Condición: As New. Unread book in perfect condition.

Kartoniert / Broschiert. Condición: New. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores the implementation of novel deep learning and CNN methodologies and their impact studies t.

Condición: New.

Condición: New.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. In English.

Hardback. Condición: New. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.…

Hardcover. Condición: Brand New. 304 pages. 9.19x6.13x9.21 inches. In Stock.

Hardback. Condición: New. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer's, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.…

Taschenbuch. Condición: Neu. Neuware - Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.…

Paperback. Condición: new. Paperback. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimers, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the readers head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimers, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the readers head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimers, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the readers head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics. This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ngangbam Herojit Singh is presently working as an Assistant Professor at NIT Agartala in the Department of Computer Science and Engineering. He received his Ph.D. degree from the NIT Manipur, and M.Tech and B.Tech from Anna University, Chennai. Hi.…