Soft Computing Based Medical Image Analysis

Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions. Covers numerous soft computing approaches, including fuzzy logic, neural networks, evolutionary computing, rough sets and Swarm intelligence Presents transverse research in soft computing formation from various engineering and industrial sectors in the medical domain Highlights challenges and the future scope for soft computing based medical analysis and processing techniques

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  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Pages : 292 pages
  • ISBN : 0128131748
  • Rating : 5/5 from 1 reviews
CLICK HERE TO GET THIS BOOKSoft Computing Based Medical Image Analysis

Soft Computing Based Medical Image Analysis

Soft Computing Based Medical Image Analysis
  • Author : Nilanjan Dey,Amira Ashour,Fuquian Shi,Valentina E. Balas
  • Publisher : Academic Press
  • Release : 18 January 2018
GET THIS BOOKSoft Computing Based Medical Image Analysis

Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies

Histopathological Image Analysis in Medical Decision Making

Histopathological Image Analysis in Medical Decision Making
  • Author : Dey, Nilanjan,Ashour, Amira S.,Kalia, Harihar,Goswami, R.T.,Das, Himansu
  • Publisher : IGI Global
  • Release : 21 September 2018
GET THIS BOOKHistopathological Image Analysis in Medical Decision Making

Medical imaging technologies play a significant role in visualization and interpretation methods in medical diagnosis and practice using decision making, pattern classification, diagnosis, and learning. Progressions in the field of medical imaging lead to interdisciplinary discovery in microscopic image processing and computer-assisted diagnosis systems, and aids physicians in the diagnosis and early detection of diseases. Histopathological Image Analysis in Medical Decision Making provides emerging research exploring the theoretical and practical applications of image technologies and feature extraction procedures within the

Medical Big Data and Internet of Medical Things

Medical Big Data and Internet of Medical Things
  • Author : Aboul Ella Hassanien,Nilanjan Dey,Surekha Borra
  • Publisher : CRC Press
  • Release : 25 October 2018
GET THIS BOOKMedical Big Data and Internet of Medical Things

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great

Deep Learning for Chest Radiographs

Deep Learning for Chest Radiographs
  • Author : Yashvi Chandola,Jitendra Virmani,H.S Bhadauria,Papendra Kumar
  • Publisher : Elsevier
  • Release : 16 July 2021
GET THIS BOOKDeep Learning for Chest Radiographs

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent

Neutrosophic Set in Medical Image Analysis

Neutrosophic Set in Medical Image Analysis
  • Author : Yanhui Guo,Amira S. Ashour
  • Publisher : Academic Press
  • Release : 08 August 2019
GET THIS BOOKNeutrosophic Set in Medical Image Analysis

Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set’s novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to

A Beginner’s Guide to Image Shape Feature Extraction Techniques

A Beginner’s Guide to Image Shape Feature Extraction Techniques
  • Author : Jyotismita Chaki,Nilanjan Dey
  • Publisher : CRC Press
  • Release : 25 July 2019
GET THIS BOOKA Beginner’s Guide to Image Shape Feature Extraction Techniques

This book emphasizes various image shape feature extraction methods which are necessary for image shape recognition and classification. Focussing on a shape feature extraction technique used in content-based image retrieval (CBIR), it explains different applications of image shape features in the field of content-based image retrieval. Showcasing useful applications and illustrating examples in many interdisciplinary fields, the present book is aimed at researchers and graduate students in electrical engineering, data science, computer science, medicine, and machine learning including medical physics

Soft Computing: Theories and Applications

Soft Computing: Theories and Applications
  • Author : Millie Pant,Tarun Kumar Sharma,Rajeev Arya,B.C. Sahana,Hossein Zolfagharinia
  • Publisher : Springer Nature
  • Release : 29 June 2020
GET THIS BOOKSoft Computing: Theories and Applications

This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2019), organized by the National Institute of Technology Patna, India. Offering valuable insights into soft computing for teachers and researchers alike, the book will inspire further research in this dynamic field.

MEDICAL IMAGE PROCESSING

MEDICAL IMAGE PROCESSING
  • Author : G.R. SINHA,BHAGWATI CHARAN PATEL
  • Publisher : PHI Learning Pvt. Ltd.
  • Release : 20 January 2014
GET THIS BOOKMEDICAL IMAGE PROCESSING

Medical Image Processing: Concepts and Applications presents an overview of image processing for various applications in the field of medical science. Inclusion of several topics like noise reduction filters, feature extraction, image restoration, segmentation, soft computing techniques and context-based medical image retrieval, etc. makes this book a single-source information meeting the requirements of the readers. Besides, the coverage of digital image processing, human visual perception and CAD system to be used in automated diagnosis system, medical imaging modalities, various application

Level Set Method in Medical Imaging Segmentation

Level Set Method in Medical Imaging Segmentation
  • Author : Ayman El-Baz,Jasjit S. Suri
  • Publisher : CRC Press
  • Release : 26 June 2019
GET THIS BOOKLevel Set Method in Medical Imaging Segmentation

Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based

Advances in Computational and Bio-Engineering

Advances in Computational and Bio-Engineering
  • Author : S. Jyothi,D. M. Mamatha,Suresh Chandra Satapathy,K. Srujan Raju,Margarita N. Favorskaya
  • Publisher : Springer Nature
  • Release : 06 July 2020
GET THIS BOOKAdvances in Computational and Bio-Engineering

This book gathers state-of-the-art research in computational engineering and bioengineering to facilitate knowledge exchange between various scientific communities. Computational engineering (CE) is a relatively new discipline that addresses the development and application of computational models and simulations often coupled with high-performance computing to solve complex physical problems arising in engineering analysis and design in the context of natural phenomena. Bioengineering (BE) is an important aspect of computational biology, which aims to develop and use efficient algorithms, data structures, and visualization

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems
  • Author : Arun Kumar Sangaiah
  • Publisher : Academic Press
  • Release : 26 July 2019
GET THIS BOOKDeep Learning and Parallel Computing Environment for Bioengineering Systems

Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major

Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis
  • Author : Nilanjan Dey,Sanjeev Wagh,Parikshit N. Mahalle,Mohd. Shafi Pathan
  • Publisher : CRC Press
  • Release : 20 May 2019
GET THIS BOOKApplied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security,

Data Science and Analytics

Data Science and Analytics
  • Author : Usha Batra,Nihar Ranjan Roy,Brajendra Panda
  • Publisher : Springer Nature
  • Release : 27 May 2020
GET THIS BOOKData Science and Analytics

This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.

Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development

Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development
  • Author : Sandeep Kumar,Anand Nayyar,Anand Paul
  • Publisher : CRC Press
  • Release : 05 December 2019
GET THIS BOOKSwarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development

Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
  • Author : Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
  • Publisher : Academic Press
  • Release : 30 November 2018
GET THIS BOOKMachine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as