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Author : Pravin Kshirsagar,Salim Chavan,Sudhir Akojwar
Publisher : Unknown
Page : 104 pages
File Size : 49,9 Mb
Release : 2017-04-18
Category : Electronic
ISBN : 3330651059
Author : Fatih ÖZYURT,Eser SERT,Engin AVCI,Esin DOGANTEKIN
Publisher : Infinite Study
Page : 16 pages
File Size : 41,9 Mb
Release : 2024-07-02
Category : Mathematics
ISBN : 8210379456XXX
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Author : Mubashir Tariq,Aized Amin Sufi,Habib ur Rehman,Syed Kamran Ajmal,Danish Riaz,Muhammad Amjad,Bilal Ahmad,Muhammad Haris Raza
Publisher : Infinite Study
Page : 25 pages
File Size : 44,6 Mb
Release : 2022-01-01
Category : Computers
ISBN : 8210379456XXX
In the domain of Medical Image Analysis (MIA), it is difficult to perform brain tumor classification. With the help of machine learning technology and algorithms, brain tumor can be easily diagnosed by the radiologists without practicing any surgical approach. In the previous few years, remarkable progress has been observed by deep learning techniques in the domain of MIA. Although, the classification of brain tumor through Magnetic Resonance Imaging (MRI) has seen multiple problems: 1) the structure of brain and complexity of brain tissues; 2) deriving the classification of brain tumor due to brain’s nature of high-density. To study the classification of brain tumor; inculcating the normal and abnormal MRI, this study has designed a blended method by using Neutrosophic Super Resolution (NSR) with Fuzzy-C-Means (FCM) and Convolutional Neural Network (CNN).Initially, non-local mean filtered MRI provided Neutrosophic Super Resolution (NSR) image, however, for enhancement of clustering and simulation of the brain tumor along with the reduction of time consumption, efficiency and accuracy without any technical hindrance Support vector Machine (SVM) guided FCM was applied. Consequently, the recommended method resulted in an excellent performance with 98.12%, 98.2% of average success about sensitivity and 1.8% of error rate brain tumor image.
Author : IEEE Staff
Publisher : Unknown
Page : 128 pages
File Size : 42,6 Mb
Release : 2020-03-18
Category : Electronic
ISBN : 1728159059
Communications technology Communication equipment Radio communication equipment Telephone equipment Computer network management Computer networks Power electronics Modular multilevel converters Pulse width modulation converters Computers and information processing Image processing Image classification Spatial resolution
Author : Dr. Sasidhar B
Publisher : Archers & Elevators Publishing House
Page : 85 pages
File Size : 50,6 Mb
Release : 2024-07-02
Category : Antiques & Collectibles
ISBN : 9788119385492
Author : Rajiv Misra,Rudrapatna K. Shyamasundar,Amrita Chaturvedi,Rana Omer
Publisher : Springer Nature
Page : 362 pages
File Size : 54,8 Mb
Release : 2021-09-29
Category : Computers
ISBN : 9783030824693
This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.
Author : Natarajan Meghanathan,Dhinaharan Nagamalai,Nabendu Chaki
Publisher : Springer Science & Business Media
Page : 901 pages
File Size : 54,5 Mb
Release : 2012-08-11
Category : Technology & Engineering
ISBN : 9783642315527
The international conference on Advances in Computing and Information technology (ACITY 2012) provides an excellent international forum for both academics and professionals for sharing knowledge and results in theory, methodology and applications of Computer Science and Information Technology. The Second International Conference on Advances in Computing and Information technology (ACITY 2012), held in Chennai, India, during July 13-15, 2012, covered a number of topics in all major fields of Computer Science and Information Technology including: networking and communications, network security and applications, web and internet computing, ubiquitous computing, algorithms, bioinformatics, digital image processing and pattern recognition, artificial intelligence, soft computing and applications. Upon a strength review process, a number of high-quality, presenting not only innovative ideas but also a founded evaluation and a strong argumentation of the same, were selected and collected in the present proceedings, that is composed of three different volumes.
Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 45,6 Mb
Release : 2019
Category : Electronic
ISBN : 1728134455
Author : H. S. Saini,Rishi Sayal,Aliseri Govardhan,Rajkumar Buyya
Publisher : Springer
Page : 607 pages
File Size : 49,8 Mb
Release : 2019-06-18
Category : Technology & Engineering
ISBN : 9789811370823
This book includes high-quality, peer-reviewed research papers from the 6thInternational Conference on Innovations in Computer Science & Engineering (ICICSE 2018), held at Guru Nanak Institutions, Hyderabad, India from August 17 to 18, 2018. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques and offers a platform for researchers from academia and industry to present their original work and exchange ideas, information, techniques and applications in the field of computer science.
Author : Jyotismita Chaki
Publisher : Academic Press
Page : 260 pages
File Size : 40,7 Mb
Release : 2021-11-27
Category : Science
ISBN : 9780323983952
Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Author : Sujata Dash,Subhendu Kumar Pani,Ajith Abraham,Yulan Liang
Publisher : Springer Nature
Page : 443 pages
File Size : 52,8 Mb
Release : 2021-11-05
Category : Technology & Engineering
ISBN : 9783030756574
This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.
Author : Bjoern Menze,Spyridon Bakas
Publisher : Frontiers Media SA
Page : 324 pages
File Size : 54,5 Mb
Release : 2021-08-10
Category : Science
ISBN : 9782889711703
Author : Ilias Maglogiannis,Lazaros Iliadis,Elias Pimenidis
Publisher : Springer
Page : 458 pages
File Size : 45,9 Mb
Release : 2020-05-30
Category : Computers
ISBN : 3030491854
This 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.* The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: Part I: classification; clustering - unsupervised learning -analytics; image processing; learning algorithms; neural network modeling; object tracking - object detection systems; ontologies - AI; and sentiment analysis - recommender systems. Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language. *The conference was held virtually due to the COVID-19 pandemic.
Author : Lenka Lhotská,Lucie Sukupova,Igor Lacković,Geoffrey S. Ibbott
Publisher : Unknown
Page : 128 pages
File Size : 54,8 Mb
Release : 2019
Category : Biomedical engineering
ISBN : 981109036X
This book presents the proceedings of the IUPESM World Congress on Biomedical Engineering and Medical Physics, a tri-annual high-level policy meeting dedicated exclusively to furthering the role of biomedical engineering and medical physics in medicine. The book offers papers about emerging issues related to the development and sustainability of the role and impact of medical physicists and biomedical engineers in medicine and healthcare. It provides a unique and important forum to secure a coordinated, multileveled global response to the need, demand, and importance of creating and supporting strong academic and clinical teams of biomedical engineers and medical physicists for the benefit of human health.
Author : Yuriy Zaychenko,Galib Hamidov,Bohdan Chapaliuk
Publisher : Cambridge Scholars Publishing
Page : 133 pages
File Size : 44,6 Mb
Release : 2023-07-26
Category : Medical
ISBN : 9781527515406
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.