Recent Trends in Computer-aided Diagnostic Systems for Skin Diseases (e-bog) af Munshi, Sugata
Munshi, Sugata (forfatter)

Recent Trends in Computer-aided Diagnostic Systems for Skin Diseases e-bog

1094,57 DKK (inkl. moms 1368,21 DKK)
Recent Trends in Computer-aided Diagnostic Systems for Skin Diseases: Theory, Implementation, and Analysis provides comprehensive coverage on the development of computer-aided diagnostic (CAD) systems employing image processing and machine learning tools for improved, uniform evaluation and diagnosis (avoiding subjective judgment) of skin disorders. The methods and tools are described in a ge...
E-bog 1094,57 DKK
Forfattere Munshi, Sugata (forfatter)
Udgivet 7 november 2021
Længde 204 sider
Genrer Anatomy
Sprog English
Format epub
Beskyttelse LCP
ISBN 9780323914666
Recent Trends in Computer-aided Diagnostic Systems for Skin Diseases: Theory, Implementation, and Analysis provides comprehensive coverage on the development of computer-aided diagnostic (CAD) systems employing image processing and machine learning tools for improved, uniform evaluation and diagnosis (avoiding subjective judgment) of skin disorders. The methods and tools are described in a general way so that these tools can be applied not only for skin diseases but also for a wide range of analogous problems in the domain of biomedical systems. Moreover, quantification of clinically relevant information that can associate the findings of physicians/experts is the most challenging task of any CAD system. This book gives all the details in a step-by-step form for different modules so that the readers can develop each of the modules like preprocessing, feature extraction/learning, disease classification, as well as an entire expert diagnosis system themselves for their own applications. Demonstrates extensive calculations for illustrating the theoretical analysis of advanced image processing and machine learning techniques Provides a comprehensive coverage on the development of various signal processing tools for the extraction of statistical and clinically correlated features from skin lesion images Describes image processing and machine learning techniques for improved uniform evaluation and diagnosis of skin disorders