Advanced Machine Learning Approaches In Cancer Prognosis PDF Download
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| Author | : Janmenjoy Nayak |
| Publisher | : Springer Nature |
| Total Pages | : 461 |
| Release | : 2021-05-29 |
| Genre | : Technology & Engineering |
| ISBN | : 3030719758 |
Download Advanced Machine Learning Approaches in Cancer Prognosis Book in PDF, ePub and Kindle
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.
| Author | : Utku Kose |
| Publisher | : Springer Nature |
| Total Pages | : 311 |
| Release | : 2020-09-12 |
| Genre | : Technology & Engineering |
| ISBN | : 9811563217 |
Download Deep Learning for Cancer Diagnosis Book in PDF, ePub and Kindle
This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
| Author | : Andrew Davies |
| Publisher | : |
| Total Pages | : |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Download Machine Learning Approaches To Prognostication In Supportive Care In Cancer Book in PDF, ePub and Kindle
MACHINE LEARNING APPROACHES TO PROGNOSTICATION IN SUPPORTIVE CARE IN CANCERIntroductionSurvival prediction is an important aspect of supportive care, and especially palliative care. However, robust survival prediction remains elusive. Machine learning approaches offer the potential to identify novel prognostic indicators, and so to develop more robust prognostic algorithms. Objectives The objective of this feasibility study was to develop a prognostic algorithm using machine learning for testing in a definitive study.Methods 50 patients with advanced cancer and an estimated prognosis of
| Author | : Meenu Gupta |
| Publisher | : CRC Press |
| Total Pages | : 202 |
| Release | : 2021-12-31 |
| Genre | : Computers |
| ISBN | : 1000508668 |
Download Cancer Prediction for Industrial IoT 4.0 Book in PDF, ePub and Kindle
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 | : Rabinarayan Satpathy |
| Publisher | : John Wiley & Sons |
| Total Pages | : 433 |
| Release | : 2021-01-20 |
| Genre | : Computers |
| ISBN | : 111978560X |
Download Data Analytics in Bioinformatics Book in PDF, ePub and Kindle
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.
| Author | : Shahid Mumtaz |
| Publisher | : Frontiers Media SA |
| Total Pages | : 173 |
| Release | : 2023-03-13 |
| Genre | : Medical |
| ISBN | : 283251569X |
Download Deep learning approaches in image-guided diagnosis for tumors Book in PDF, ePub and Kindle
| Author | : Conor Whitley |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download The Development of Machine Learning Methods for Head and Neck Cancer Prognosis Book in PDF, ePub and Kindle
| Author | : Sudipta Roy |
| Publisher | : Springer Nature |
| Total Pages | : 317 |
| Release | : 2021-04-22 |
| Genre | : Technology & Engineering |
| ISBN | : 9811605386 |
Download Advanced Prognostic Predictive Modelling in Healthcare Data Analytics Book in PDF, ePub and Kindle
This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
| Author | : Issam El Naqa |
| Publisher | : Springer |
| Total Pages | : 336 |
| Release | : 2015-06-19 |
| Genre | : Medical |
| ISBN | : 3319183052 |
Download Machine Learning in Radiation Oncology Book in PDF, ePub and Kindle
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
| Author | : Issam El Naqa |
| Publisher | : Springer Nature |
| Total Pages | : 514 |
| Release | : 2022-02-02 |
| Genre | : Science |
| ISBN | : 3030830470 |
Download Machine and Deep Learning in Oncology, Medical Physics and Radiology Book in PDF, ePub and Kindle
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
