Buch, Englisch, 300 Seiten, Hardback, Format (B × H): 183 mm x 260 mm, Gewicht: 785 g
Buch, Englisch, 300 Seiten, Hardback, Format (B × H): 183 mm x 260 mm, Gewicht: 785 g
ISBN: 978-1-7998-9194-9
Verlag: Business Science Reference
Artificial intelligence (AI) has been playing significant role in rapidly emerging healthcare sector in terms of computer-aided diagnosis (CAD), software algorithms, hardware implementation, and applications in a medical heath care. During Covid-19 pandemic, we have witnessed the role of computational intelligence and CAD systems in effective treatment of Corona virus affected people. The goal of this book is to explore the state-of-the-art of computational intelligence approaches in medical data and to classify existing Computational techniques used in medical areas as single or hybrid. The constraints of traditional healthcare system are addressed by using CAD and computationally intelligence medical data (CIMD).
This book explores the most recent developments of AI in biomedical and biomedicine fields, in the form of deep learning, artificial neural networks, biomedical information processing, biomedical research, evolutionary computing, and statistical technologies. Clinical flow diagram to aid physicians, and laboratory professionals in the management of COVID-19 patients with aspergillosis, candidiasis, mucormycosis, or cryptococcosis as co-morbidities. In addition, we can investigate how deep learning models can be used to detect COVID-19 patients in chest radiography images. Early research discovered specific abnormalities in COVID-19, aspergillosis-infected people' chest and body X–ray CT scans. The aim of this book is to bring out a critical study to investigate the applicability of convolutional neural networks (CNNs) for healthcare COVID-19 detection in chest X-ray and CT scan images, as well as to highlight the issues with using CNN directly on the entire image.
Nadda / Tyagi / Singh
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This book explores the most recent developments of AI in biomedical and biomedicine fields, in the form of deep learning, artificial neural networks, biomedical information processing, biomedical research, evolutionary computing, and statistical technologies. Clinical flow diagram to aid physicians, and laboratory professionals in the management of COVID-19 patients with aspergillosis, candidiasis, mucormycosis, or cryptococcosis as co-morbidities. In addition, we can investigate how deep learning models can be used to detect COVID-19 patients in chest radiography images. Early research discovered specific abnormalities in COVID-19, aspergillosis-infected people' chest and body X–ray CT scans. The aim of this book is to bring out a critical study to investigate the applicability of convolutional neural networks (CNNs) for healthcare COVID-19 detection in chest X-ray and CT scan images, as well as to highlight the issues with using CNN directly on the entire image.
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