Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems PDF Download
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Author: E. Priya
Publisher: Springer Nature
ISBN: 9811561419
Category : Medical
Languages : en
Pages : 290
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Book Description
This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
Author: E. Priya
Publisher: Springer Nature
ISBN: 9811561419
Category : Medical
Languages : en
Pages : 290
Get Book Here
Book Description
This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.
Author: Sanjana Balasubramanian
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Today there are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. Early detection of this fatal disease is very important which helps in decreasing the morality rate and increasing the survival period of breast cancer patients. The project uses Mammography which is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. To detect breast cancer in mammogram, image segmentation is performed with the help of Fuzzy C-means (FCM) technique. Further those segmented regions features are extracted, and it is trained completely, finally trained images are classified by the efficient classifier of different classes in mammogram. Texture features are extracted using a feature extraction technique like Multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), Gray-level Co-occurrence Matrix (GLCM). Morphological operators are used to distinguish masses and micro calcifications from the background tissue and KNN algorithm is used for classification. The boundaries of tumor affected region in mammogram are marked and displayed to the doctor, along with area of tumor.
Author: Meenu Gupta
Publisher: CRC Press
ISBN: 1000508668
Category : Computers
Languages : en
Pages : 202
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Book Description
Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective explores various cancers using Artificial Intelligence techniques. It presents the rapid advancement in the existing prediction models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment options are discussed, including specific ideas, tools and practices most applicable to product/service development and innovation opportunities. The wide variety of topics covered offers readers multiple perspectives on various disciplines. Features • Covers the fundamentals, history, reality and challenges of cancer • Presents concepts and analysis of different cancers in humans • Discusses Machine Learning-based deep learning and data mining concepts in the prediction of cancer • Offers real-world examples of cancer prediction • Reviews strategies and tools used in cancer prediction • Explores the future prospects in cancer prediction and treatment Readers will learn the fundamental concepts and analysis of cancer prediction and treatment, including how to apply emerging technologies such as Machine Learning into practice to tackle challenges in domains/fields of cancer with real-world scenarios. Hands-on chapters contributed by academicians and other professionals from reputed organizations provide and describe frameworks, applications, best practices and case studies on emerging cancer treatment and predictions. This book will be a vital resource to graduate students, data scientists, Machine Learning researchers, medical professionals and analytics managers.
Author: Mengjie Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
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Book Description
Breast cancer, mostly occurring in women, is the mostly frequently diagnosed cancer. Early detection based on phenotype and genotype features can greatly increases the chances for successful treatment. In this report, four different machine learning algorithms were tested for breast cancer prediction. Principal component analysis was used to reduce dimension for the original correlated dataset. The results show that KNN, SVM with linear kernel and Logistic Regression outperform Naive Bayes with very similar accuracy. KNN achieved the highest average accuracy of 0.9756 after 10 fold cross-validation when k equals to 7. The highest AUC value of 0.9944 was achieved by SVM with linear kernel. The results also show that increasing number of top eigenvectors increases the prediction accuracy, however, as the eigenvector number goes above a certain threshold, it adds more noise instead of signal.
Author: J. Ross Quinlan
Publisher: Morgan Kaufmann
ISBN: 9781558602380
Category : Computers
Languages : en
Pages : 286
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Book Description
This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.
Author: IEEE Staff
Publisher:
ISBN: 9781538694404
Category :
Languages : en
Pages :
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Book Description
ICOEI 2019 will provide an outstanding international forum for sharing knowledge and results in all fields of Engineering and Technology The primary goal of the conference is to promote research and developmental activities in Electronics and Informatics Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in India and abroad The conference is organized to make it an ideal platform for people to share views and experiences in Electronics, Informatics and related areas
Author: Amit Kumar
Publisher: Springer Nature
ISBN: 9811636907
Category : Technology & Engineering
Languages : en
Pages : 1600
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Book Description
This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
Author: Daniel T. Larose
Publisher: John Wiley & Sons
ISBN: 0471687537
Category : Computers
Languages : en
Pages : 240
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Book Description
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Author: Christoph I. Lee
Publisher: Oxford University Press
ISBN: 0190270268
Category : Medical
Languages : en
Pages : 545
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Book Description
Breast Imaging presents a comprehensive review of the subject matter commonly encountered by practicing radiologists and radiology residents in training. This volume includes succinct overviews of breast cancer epidemiology, screening, staging, and treatment; overviews of all imaging modalities including mammography, tomosynthesis, ultrasound, and MRI; step-by-step approaches for image-guided breast interventions; and high-yield chapters organized by specific imaging finding seen on mammography, tomosynthesis, ultrasound, and MRI. Part of the Rotations in Radiology series, this book offers a guided approach to breast imaging interpretation and techniques, highlighting the nuances necessary to arrive at the best diagnosis and management. Each chapter contains a targeted discussion of an imaging finding which reviews the anatomy and physiology, distinguishing features, imaging techniques, differential diagnosis, clinical issues, key points, and further reading. Breast Imaging is a must-read for residents and practicing radiologists seeking a foundation for the essential knowledge base in breast imaging.
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
ISBN: 111978560X
Category : Computers
Languages : en
Pages : 433
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Book Description
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
