Machine Learning in Medical Imaging and Computer Vision
$10.00Amita Nandal, Liang Zhou, Arvind Dhaka
ISBNs: 9781839535932, 978-1839535932, 978-1-83953-593-2, 978-1-83953-594-9, 978-1839535949, 9781839535949
English | 2024 | PDF | 21 MB | 382 Pages
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.
