Название: Machine Learning in Medical Imaging and Computer VisionАвтор: Amita Nandal, Liang Zhou, Arvind Dhaka, Todor Ganchev
Издательство: The Institution of Engineering and Technology
Год: 2024
Страниц: 382
Язык: английский
Формат: pdf (true)
Размер: 21.1 MB
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.
Medical imaging is increasingly using Machine Learning and computer vision. Deep Learning allows convolutional neural networks (CNNs) to classify, segment, and identify medical images. This has allowed the creation of new tools and apps to aid in illness detection and treatment. This discipline studies deep learning-enabled medical computer vision, Machine Learning for medical image analysis, and personalized medicine using image processing, computer vision, and Machine Learning. Machine Learning and computer vision may enhance medical imaging and
healthcare, and they will likely become more vital.
Chapter 1 presents Machine Learning algorithms and applications in medical imaging processing. In this chapter authors discuss several applications using a CNN for medical image quality assessment. It shows that Machine Learning can identify and handle both diagnostic and non-diagnostic images.
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.
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