Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.
Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges
Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications
Introduces several techniques for medical image processing and analysis for CAD systems design
Название: Computer-aided design and diagnosis methods for biomedical applications ISBN: 0367638835 ISBN-13(EAN): 9780367638832 Издательство: Taylor&Francis Рейтинг: Цена: 137810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Computer-aided design (CAD) plays a key role in improving biomedical systems for various applications. It also helps in the detection, identification, predication, analysis, and classification of diseases, in the management of chronic conditions, and in the delivery of health services. This book discusses the uses of CAD to solve real-world problems and challenges in biomedical systems with the help of appropriate case studies and research simulation results. Aiming to overcome the gap between CAD and biomedical science, it describes behaviors, concepts, fundamentals, principles, case studies, and future directions for research, including the automatic identification of related disorders using CAD. Features:Proposes CAD for the study of biomedical signals to understand physiology and to improve healthcare systems’ ability to diagnose and identify health disorders. Presents concepts of CAD for biomedical modalities in different disorders. Discusses design and simulation examples, issues, and challenges. Illustrates bio-potential signals and their appropriate use in studying different disorders. Includes case studies, practical examples, and research directions. Computer-Aided Design and Diagnosis Methods for Biometrical Applications is aimed at researchers, graduate students in biomedical engineering, image processing, biomedical technology, medical imaging, and health informatics.
An introduction to the computer-based modeling of influenza, a continuing major worldwide communicable disease.
The use of (1) as an illustration of a methodology for the computer-based modeling of communicable diseases.
For the purposes of (1) and (2), a basic influenza model is formulated as a system of partial differential equations (PDEs) that define the spatiotemporal evolution of four populations: susceptibles, untreated and treated infecteds, and recovereds. The requirements of a well-posed PDE model are considered, including the initial and boundary conditions. The terms of the PDEs are explained.
The computer implementation of the model is illustrated with a detailed line-by-line explanation of a system of routines in R (a quality, open-source scientific computing system that is readily available from the Internet). The R routines demonstrate the straightforward numerical solution of a system of nonlinear PDEs by the method of lines (MOL), an established general algorithm for PDEs.
The presentation of the PDE modeling methodology is introductory with a minumum of formal mathematics (no theorems and proofs), and with emphasis on example applications. The intent of the book is to assist in the initial understanding and use of PDE mathematical modeling of communicable diseases, and the explanation and interpretation of the computed model solutions, as illustrated with the influenza model.
An introduction to the computer-based modeling of influenza, a continuing major worldwide communicable disease.
The use of (1) as an illustration of a methodology for the computer-based modeling of communicable diseases.
For the purposes of (1) and (2), a basic influenza model is formulated as a system of partial differential equations (PDEs) that define the spatiotemporal evolution of four populations: susceptibles, untreated and treated infecteds, and recovereds. The requirements of a well-posed PDE model are considered, including the initial and boundary conditions. The terms of the PDEs are explained.
The computer implementation of the model is illustrated with a detailed line-by-line explanation of a system of routines in R (a quality, open-source scientific computing system that is readily available from the Internet). The R routines demonstrate the straightforward numerical solution of a system of nonlinear PDEs by the method of lines (MOL), an established general algorithm for PDEs.
The presentation of the PDE modeling methodology is introductory with a minumum of formal mathematics (no theorems and proofs), and with emphasis on example applications. The intent of the book is to assist in the initial understanding and use of PDE mathematical modeling of communicable diseases, and the explanation and interpretation of the computed model solutions, as illustrated with the influenza model.
Автор: Jacob Scharcanski; M. Emre Celebi Название: Computer Vision Techniques for the Diagnosis of Skin Cancer ISBN: 3642396070 ISBN-13(EAN): 9783642396076 Издательство: Springer Рейтинг: Цена: 121890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for patients with multiple atypical nevi.
Автор: Jacob Scharcanski; M. Emre Celebi Название: Computer Vision Techniques for the Diagnosis of Skin Cancer ISBN: 3662522624 ISBN-13(EAN): 9783662522622 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for patients with multiple atypical nevi.
Автор: Xiao-Xia Yin; Sillas Hadjiloucas; Yanchun Zhang Название: Pattern Classification of Medical Images: Computer Aided Diagnosis ISBN: 3319570269 ISBN-13(EAN): 9783319570266 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis.
Название: Computer-aided detection and diagnosis in medical imaging ISBN: 036778355X ISBN-13(EAN): 9780367783556 Издательство: Taylor&Francis Рейтинг: Цена: 45930.00 T Наличие на складе: Невозможна поставка. Описание: This book covers the major technical advances and methodologies shaping the development and clinical utility of CAD systems in breast imaging, chest imaging, abdominal imaging, and other emerging applications. The first section presents CAD technologies in breast imaging and the second section discusses CAD technologies in chest and abdominal im
Автор: Gasm Elseid, Arwa Ahmed , Mohammed Hamza, Alnazie Название: Computer-Aided Glaucoma Diagnosis System ISBN: 0367406268 ISBN-13(EAN): 9780367406264 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Невозможна поставка. Описание: Glaucoma is the second leading cause of blindness globally. Early detection and treatment can prevent its progression to avoid total blindness. This book discusses and reviews current approaches for detection and examines new approaches for diagnosis using CAD and machine learning techniques.
Автор: Yin Xiao-Xia, Hadjiloucas Sillas, Zhang Yanchun Название: Pattern Classification of Medical Images: Computer Aided Diagnosis ISBN: 3319860615 ISBN-13(EAN): 9783319860619 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis.
Автор: Su Ruidan, Liu Han Название: Medical Imaging and Computer-Aided Diagnosis: Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (Micad 2020) ISBN: 9811551987 ISBN-13(EAN): 9789811551987 Издательство: Springer Рейтинг: Цена: 232910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike.
Автор: Shantanu Banik, Rangaraj Rangayyan, J.E. Leo Desautels Название: Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer ISBN: 1627050825 ISBN-13(EAN): 9781627050821 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 46200.00 T Наличие на складе: Невозможна поставка. Описание: Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and localization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages.
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