Computational Methods and Deep Learning for Ophthalmology
Computational Methods and Deep Learning for Ophthalmology

Author: D. Jude Hemanth

Publisher: Elsevier

Published: 2023-02-18

Total Pages: 252

ISBN-13: 0323954146

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Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration. - Presents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye - Conveys the role of a variety of computational methods and algorithms for efficient and effective diagnosis of ophthalmologic disorders, including Diabetic Retinopathy, Glaucoma, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders - Explains how to develop and apply a variety of computational diagnosis systems and technologies, including medical image processing algorithms, bioinspired optimization, Deep Learning, computational intelligence systems, fuzzy-based segmentation methods, transfer learning approaches, and hybrid Artificial Neural Networks

Computational Methods and Deep Learning for Ophthalmology
Computational Methods and Deep Learning for Ophthalmology

Author: D. Jude Hemanth

Publisher: Elsevier

Published: 2023-03

Total Pages: 250

ISBN-13: 0323954154

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Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration.

Artificial Intelligence in Ophthalmology
Artificial Intelligence in Ophthalmology

Author: Andrzej Grzybowski

Publisher: Springer Nature

Published: 2021-10-13

Total Pages: 280

ISBN-13: 3030786013

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This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

Deep Learning for Biomedical Applications
Deep Learning for Biomedical Applications

Author: Utku Kose

Publisher: CRC Press

Published: 2021-07-19

Total Pages: 365

ISBN-13: 1000406423

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This book is a detailed reference on biomedical applications using Deep Learning. Because Deep Learning is an important actor shaping the future of Artificial Intelligence, its specific and innovative solutions for both medical and biomedical are very critical. This book provides a recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for solving biomedical problems. It focuses on different types of data (i.e. raw data, signal-time series, medical images) to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, image processing perspectives, and even genomics. It takes the reader through different sides of Deep Learning oriented solutions. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educations who are working in the context of the topics.

Explainable and Interpretable Models in Computer Vision and Machine Learning
Explainable and Interpretable Models in Computer Vision and Machine Learning

Author: Hugo Jair Escalante

Publisher: Springer

Published: 2018-11-29

Total Pages: 305

ISBN-13: 3319981315

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This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Computational Methods in Science and Technology
Computational Methods in Science and Technology

Author: Sukhpreet Kaur

Publisher: CRC Press

Published: 2024-10-10

Total Pages: 595

ISBN-13: 1040260578

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This book contains the proceedings of the 4TH International Conference on Computational Methods in Science and Technology (ICCMST 2024). The proceedings explores research and innovation in the field of Internet of things, Cloud Computing, Machine Learning, Networks, System Design and Methodologies, Big Data Analytics and Applications, ICT for Sustainable Environment, Artificial Intelligence and it provides real time assistance and security for advanced stage learners, researchers and academicians has been presented. This will be a valuable read to researchers, academicians, undergraduate students, postgraduate students, and professionals within the fields of Computer Science, Sustainability and Artificial Intelligence.

Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post COVID-19 Landscape
Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post COVID-19 Landscape

Author: G. Madhu

Publisher: Bentham Science Publishers

Published: 2022-10-20

Total Pages: 199

ISBN-13: 981507928X

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This book gives an overview of innovative approaches in telehealth and telemedicine. The Goal of the content is to inform readers about recent computer applications in e-health, including Internet of Things (IoT) and Internet of Medical Things (IoMT) technology. The 9 chapters will guide readers to determine the urgency to intervene in specific medical cases, and to assess risk to healthcare workers. The focus on telehealth along with telemedicine, encompasses a broader spectrum of remote healthcare services for the reader to understand. Chapters cover the following topics: - A COVID-19 care system for virus precaution, prevention, and treatment - The Internet of Things (IoT) in Telemedicine, - Artificial Intelligence for Remote Patient Monitoring systems - Machine Learning in Telemedicine - Convolutional Neural Networks for the detection and prediction of melanoma in skin lesions - COVID-19 virus contact tracing via mobile apps - IoT and Cloud convergence in healthcare - Lung cancer classification and detection using deep learning - Telemedicine in India This book will assist students, academics, and medical professionals in learning about cutting-edge telemedicine technologies. It will also inform beginner researchers in medicine about upcoming trends, problems, and future research paths in telehealth and telemedicine for infectious disease control and cancer diagnosis.

Convergence of Deep Learning and Internet of Things: Computing and Technology
Convergence of Deep Learning and Internet of Things: Computing and Technology

Author: Kavitha, T.

Publisher: IGI Global

Published: 2022-12-19

Total Pages: 371

ISBN-13: 166846277X

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Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

AI-Powered Advances in Pharmacology
AI-Powered Advances in Pharmacology

Author: Shaik, Aminabee

Publisher: IGI Global

Published: 2024-09-14

Total Pages: 512

ISBN-13:

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In the field of pharmaceutical sciences, the integration of artificial intelligence (AI) has emerged as a groundbreaking force, propelling the field into uncharted territories of discovery and innovation. As traditional approaches in drug discovery and development encounter new challenges, the need for cutting-edge technologies becomes increasingly apparent. AI-Powered Advances in Pharmacology offers an insightful exploration of this critical intersection between AI and pharmacological research. This book delves into how AI technologies are reshaping the understanding of diseases, predicting drug responses, and optimizing therapeutic interventions. It navigates through the relationship between AI algorithms, big data analytics, and traditional pharmacological methodologies, promising to accelerate drug development and usher in a new era of precision medicine. The primary objective of AI-Powered Advances in Pharmacology is to conduct a thorough exploration of the integration of artificial intelligence (AI) into pharmacological research, shedding light on its transformative impact on drug discovery, development, and personalized medicine. This comprehensive overview aims to serve as a valuable resource for researchers, practitioners, and students in the field, bridging the gap between traditional pharmacological approaches and AI methodologies. Through case studies and discussions of emerging trends, the book contributes to the evolving landscape of pharmacology, fostering a deeper understanding of diseases, optimizing therapeutic interventions, and shaping the future of precision medicine. By providing practical insights, it aims to inspire further advancements at the intersection of artificial intelligence and pharmacology.

Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection
Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection

Author: Arpana Parihar

Publisher: Academic Press

Published: 2022-07-13

Total Pages: 620

ISBN-13: 0323998003

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Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics compiles information about various computational bioinformatic approaches that can help combat viral infection. The book includes working knowledge of various molecular docking and molecular dynamic simulation approaches that have been exploited for drug repurposing and drug designing purpose. In addition, it sheds light on reverse vaccinomics and immunoinformatic approaches for vaccine designing against SARS-CoV2 infection. This book is an essential resource for researchers, bioinformaticians, computational biologists, computational chemists and pharmaceutical companies who are working on the development of effective and specific therapeutic interventions and point-of-care diagnostic devices using various computational approaches. - Covers computational based approaches for designing and repurposing drugs - Discusses immunoinformatic and reverse vaccinomic approaches for effective vaccine design - Categorizes information about artificial intelligence-based drug screening and diagnostic tools