The Application Of Deep Learning To Nucleus Images For Early Cancer Diagnostics
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| Author | : Ali Can Soylemezoglu |
| Publisher | : |
| Total Pages | : 72 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
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 | : Jyotismita Chaki |
| Publisher | : CRC Press |
| Total Pages | : 189 |
| Release | : 2023-02-22 |
| Genre | : Computers |
| ISBN | : 1000836150 |
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 |
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 |
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 | : Rohit Tanwar |
| Publisher | : CRC Press |
| Total Pages | : 325 |
| Release | : 2023-01-12 |
| Genre | : Computers |
| ISBN | : 1000610683 |
The volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine, providing deep insight and understanding of novel applications that address the tough questions of disease diagnosis, prevention, and immunization. The volume looks at applications of deep learning for major medical challenges such as cancer detection and identification, birth asphyxia among neonates, kidney abnormalities, white blood cell segmentation, diabetic retinopathy detection, and Covid-19 diagnosis, prevention, and immunization. The volume discusses applications of deep learning in detection, diagnosis, intensive examination and evaluation, genomic sequencing, convolutional neural networks for image recognition and processing, and more for health issues such as kidney problems, brain tumors, lung damage, and breast cancer. The authors look at ML for brain tumor segmentation, in lung CT scans, in digital X-ray devices, and for logistic and transport systems for effective delivery of healthcare.
| Author | : Madhuchanda Kar |
| Publisher | : Elsevier |
| Total Pages | : 256 |
| Release | : 2024-05-17 |
| Genre | : Computers |
| ISBN | : 0323952461 |
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer. Provides an overview of the latest developments of artificial intelligence technologies applied to the detection of pulmonary nodules Discusses the different technologies available and guides readers step-by-step to the most applicable one for the specific lung cancer type Describes the entire study design on prediction of lung cancer to help readers apply it to their research successfully
| Author | : Janmenjoy Nayak |
| Publisher | : Springer Nature |
| Total Pages | : 461 |
| Release | : 2021-05-29 |
| Genre | : Technology & Engineering |
| ISBN | : 3030719758 |
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 | : Archana Mire |
| Publisher | : CRC Press |
| Total Pages | : 169 |
| Release | : 2022-04-26 |
| Genre | : Technology & Engineering |
| ISBN | : 1000575950 |
This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.
| Author | : Yuriy Zaychenko |
| Publisher | : Cambridge Scholars Publishing |
| Total Pages | : 133 |
| Release | : 2023-07-26 |
| Genre | : Medical |
| ISBN | : 1527515400 |
This book is devoted to the problems of information technologies (IT) and artificial intelligence methods applied to medical image processing, tumour detection and cancer classification in different human organs, including the breasts, lungs and brain. The most efficient modern tools in the problem of medical images processing and analysis are considered- convolutional neural networks (CNN). The main goal of this book is to present and analyze new perspective architectures of CNN aimed to increase accuracy of cancer classification. This book contains new approaches for improving efficiency of cancer detection in comparison with known CNN structures. The numerous experimental investigations proved their better efficiency by different classification criteria as compared with known. This book will be useful to specialists engaged in IT applications in medicine, dealing with development and application of medical diagnostics systems, students and postgraduates in Computer Science, all persons who are interested in IT applications in medicine, medical personnel engaged in malignant tumour diagnostics and cancer detection, and the wider public interested in the problems of cancer diagnostics that desire to extend their knowledge of prospective IT methods and their effectively solutions.
| Author | : Sanjiban Sekhar Roy |
| Publisher | : Springer Nature |
| Total Pages | : 218 |
| Release | : 2023-07-08 |
| Genre | : Technology & Engineering |
| ISBN | : 9819937841 |
This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
