Current Applications Of Deep Learning In Cancer Diagnostics PDF Download
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| Author | : Jyotismita Chaki |
| Publisher | : CRC Press |
| Total Pages | : 189 |
| Release | : 2023-02-22 |
| Genre | : Computers |
| ISBN | : 1000836150 |
Download Current Applications of Deep Learning in Cancer Diagnostics Book in PDF, ePub and Kindle
This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.
| 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 | : Mohd Hafiz Arzmi |
| Publisher | : Springer Nature |
| Total Pages | : 41 |
| Release | : 2023-01-18 |
| Genre | : Science |
| ISBN | : 9811989370 |
Download Deep Learning in Cancer Diagnostics Book in PDF, ePub and Kindle
Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.
| 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 | : M. A. Jabbar |
| Publisher | : CRC Press |
| Total Pages | : 224 |
| Release | : 2021-09-26 |
| Genre | : Computers |
| ISBN | : 1000429083 |
Download Deep Learning in Biomedical and Health Informatics Book in PDF, ePub and Kindle
This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques. In short, the volume : Discusses the relationship between AI and healthcare, and how AI is changing the health care industry. Considers uses of deep learning in diagnosis and prediction of disease spread. Presents a comprehensive review of research applying deep learning in health informatics across multiple fields. Highlights challenges in applying deep learning in the field. Promotes research in ddeep llearning application in understanding the biomedical process. Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India. Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA. Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey. Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal. Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.
| Author | : Ali Can Soylemezoglu |
| Publisher | : |
| Total Pages | : 72 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Download The Application of Deep Learning to Nucleus Images for Early Cancer Diagnostics Book in PDF, ePub and Kindle
Cancer remains a major concern for patients and early diagnosis can go a long way in treating patients. Current cancer diagnosis usually involves a pathologist looking at tissue slices of patients for specific features associated with cancer prognosis such as nuclear morphometric measures. However, early diagnosis remains a major challenge. Recent studies have shown that changes in fibroblast nuclei play a critical role in the early development of cancer. In addition, it is crucial that computational models are capable of justifying themselves when used in critical decisions such as diagnosing a patient with cancer. In this thesis, we use machine learning techniques on two dimensional nuclei images to show that computational models are capable of presenting human interpretable features as a means of justifying themselves. In addition, we use machine learning techniques on volumetric images of nuclei of cells in a co-culture model that represents the cancer tissue microenvironment to study changes the fibroblasts undergo. These studies pave the way for various approaches to early disease diagnosis.
| Author | : Chi Hau Chen |
| Publisher | : World Scientific |
| Total Pages | : 410 |
| Release | : 2013-11-18 |
| Genre | : Computers |
| ISBN | : 9814460958 |
Download Computer Vision In Medical Imaging Book in PDF, ePub and Kindle
The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.
| Author | : Saxena, Sanjay |
| Publisher | : IGI Global |
| Total Pages | : 274 |
| Release | : 2020-10-16 |
| Genre | : Medical |
| ISBN | : 1799850722 |
Download Deep Learning Applications in Medical Imaging Book in PDF, ePub and Kindle
Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.
| Author | : Erik R. Ranschaert |
| Publisher | : Springer |
| Total Pages | : 373 |
| Release | : 2019-01-29 |
| Genre | : Medical |
| ISBN | : 3319948784 |
Download Artificial Intelligence in Medical Imaging Book in PDF, ePub and Kindle
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
| Author | : Stanley Cohen |
| Publisher | : Elsevier Health Sciences |
| Total Pages | : 290 |
| Release | : 2020-06-02 |
| Genre | : Medical |
| ISBN | : 0323675379 |
Download Artificial Intelligence and Deep Learning in Pathology Book in PDF, ePub and Kindle
Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
