Name: Machine Learning in Medical Imaging and Computer Vision (Healthcare Technologies) (en Inglés)
Price: 540.27 PEN
Availability: InStock
Author: Nandal Amita,Zhou Liang,Dhaka Arvind
ISBN: 9781839535932

portada Machine Learning in Medical Imaging and Computer Vision (Healthcare Technologies) (en Inglés)

Formato

Libro Físico

Editorial

The Institution Of Engineering And Technology

Autor

Nandal Amita,Zhou Liang,Dhaka Arvind

Idioma

Inglés

N° páginas

382

Encuadernación

Tapa Dura

ISBN13

9781839535932

Machine Learning in Medical Imaging and Computer Vision (Healthcare Technologies) (en Inglés)

Nandal Amita,Zhou Liang,Dhaka Arvind (Autor) · The Institution Of Engineering And Technology · Tapa Dura

Libro Físico

S/ 540,27

S/ 1.080,54

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Reseña del libro "Machine Learning in Medical Imaging and Computer Vision (Healthcare Technologies) (en Inglés)"

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.