Название: Machine Learning and AI Techniques in Interactive Medical Image AnalysisАвтор: Lipismita Panigrahi, Sandeep Biswal, Paolo Barsocchi
Издательство: IGI Global
Год: 2022
Страниц: 241
Язык: английский
Формат: pdf (true), epub
Размер: 28.8 MB
The healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and Machine Learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.
Chapter 1 explains the study on the deep learning algorithms and the evaluation metrics over various retinal diseases. The authors examined different state-of-the- art methods reported in recent years. The related studies were analyzed with subject to (i) rates of studies according to publishing years, (ii) the data sets used, (iii) the deep learning methods used, and (iv) diagnosed retinal diseases. The authors are also presented the impact of deep learning methods in diagnosing of diseases from images.
Chapter 2 presents the current trends of CAD (Computer Aided Detection) in modern medical imaging. This study is helpful in the detection and diagnosis of many kinds of abnormalities in medical images that are different from one another and are discovered through various studies using various imaging modalities.
Chapter 3 aims to present the workflow of computer aided medical diagnosis involved in various imaging modalities with case study on ophthalmology.
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Chapter 10 explains the use of Artificial Intelligence and Structural Magnetic Resonance Imaging in the diagnosis of major depressive disorder. The study included 18 major depressive disorder patients and 19 healthy controls. Magnetic resonance imaging has been performed using 1.5 Tesla MR unit. As a result, MR images have been classified using AI algorithms to distinguish healthy and diagnosed with MDD patients.
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