Brain Tumor Classification Using Convolutional Neural Network With Neutrosophy Super Resolution And Svm

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 : 24 pages
File Size : 51,5 Mb
Release : 2022-01-01
Category : Medical
ISBN : 8210379456XXX

Get Book

Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM by Mubashir Tariq,Aized Amin Sufi,Habib ur Rehman,Syed Kamran Ajmal,Danish Riaz,Muhammad Amjad,Bilal Ahmad,Muhammad Haris Raza Pdf

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 : Fatih ÖZYURT,Eser SERT,Engin AVCI,Esin DOGANTEKIN
Publisher : Infinite Study
Page : 16 pages
File Size : 54,6 Mb
Release : 2024-06-30
Category : Mathematics
ISBN : 8210379456XXX

Get Book

Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy by Fatih ÖZYURT,Eser SERT,Engin AVCI,Esin DOGANTEKIN Pdf

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 : 55,7 Mb
Release : 2022-01-01
Category : Computers
ISBN : 8210379456XXX

Get Book

Brain Tumor Classification Using Convolutional Neural Network with Neutrosophy, Super-Resolution and SVM by Mubashir Tariq,Aized Amin Sufi,Habib ur Rehman,Syed Kamran Ajmal,Danish Riaz,Muhammad Amjad,Bilal Ahmad,Muhammad Haris Raza Pdf

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 : Jyotismita Chaki
Publisher : Academic Press
Page : 260 pages
File Size : 49,9 Mb
Release : 2021-11-27
Category : Science
ISBN : 9780323983952

Get Book

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by Jyotismita Chaki Pdf

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 : Harish Sharma
Publisher : Springer Nature
Page : 486 pages
File Size : 54,7 Mb
Release : 2024-06-30
Category : Electronic
ISBN : 9789819720828

Get Book

Communication and Intelligent Systems by Harish Sharma Pdf

Author : Bjoern Menze,Spyridon Bakas
Publisher : Frontiers Media SA
Page : 324 pages
File Size : 40,8 Mb
Release : 2021-08-10
Category : Science
ISBN : 9782889711703

Get Book

Multimodal Brain Tumor Segmentation and Beyond by Bjoern Menze,Spyridon Bakas Pdf

Author : Pravin Kshirsagar,Salim Chavan,Sudhir Akojwar
Publisher : Unknown
Page : 104 pages
File Size : 42,5 Mb
Release : 2017-04-18
Category : Electronic
ISBN : 3330651059

Get Book

Brain Tumor Classification and Detection Using Neural Network by Pravin Kshirsagar,Salim Chavan,Sudhir Akojwar Pdf

Author : Arun Solanki,Mohd Naved
Publisher : Springer Nature
Page : 255 pages
File Size : 44,5 Mb
Release : 2023-12-15
Category : Medical
ISBN : 9783031432057

Get Book

GANs for Data Augmentation in Healthcare by Arun Solanki,Mohd Naved Pdf

Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records often different because of the cost of obtaining information and the time-consuming information. In general, clinical data are unreliable, the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue. Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information with data. This is a beneficial clinical application of GAN because it can effectively protect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.

Author : Khaled Rabie,Chandran Karthik,Subrata Chowdhury,Pushan Kumar Dutta
Publisher : IET
Page : 375 pages
File Size : 50,7 Mb
Release : 2023-10
Category : Computers
ISBN : 9781839537936

Get Book

Deep Learning in Medical Image Processing and Analysis by Khaled Rabie,Chandran Karthik,Subrata Chowdhury,Pushan Kumar Dutta Pdf

This book introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book discusses multimedia data analysis algorithms and the principles of feature selection, optimisation and analysis.

Author : Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun
Publisher : Springer Nature
Page : 187 pages
File Size : 42,7 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031018213

Get Book

A Guide to Convolutional Neural Networks for Computer Vision by Salman Khan,Hossein Rahmani,Syed Afaq Ali Shah,Mohammed Bennamoun Pdf

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Author : Eser SERT,Derya AVCI
Publisher : Infinite Study
Page : 13 pages
File Size : 47,7 Mb
Release : 2024-06-30
Category : Mathematics
ISBN : 8210379456XXX

Get Book

A new edge detection approach via neutrosophy based on maximum norm entropy by Eser SERT,Derya AVCI Pdf

In this study, a new edge detection method based on Neutrosophic Set (NS) struc- ture via using maximum norm entropy (EDA-MNE) is proposed.

Author : D.J. Hemanth,V. Vieira Estrela
Publisher : IOS Press
Page : 284 pages
File Size : 54,8 Mb
Release : 2017-12
Category : Computers
ISBN : 9781614998228

Get Book

Deep Learning for Image Processing Applications by D.J. Hemanth,V. Vieira Estrela Pdf

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Author : J. Jayakumari,George K. Karagiannidis,Maode Ma,Syed Akhter Hossain
Publisher : Springer Nature
Page : 837 pages
File Size : 54,8 Mb
Release : 2020-06-13
Category : Technology & Engineering
ISBN : 9789811539923

Get Book

Advances in Communication Systems and Networks by J. Jayakumari,George K. Karagiannidis,Maode Ma,Syed Akhter Hossain Pdf

This book presents the selected peer-reviewed papers from the International Conference on Communication Systems and Networks (ComNet) 2019. Highlighting the latest findings, ideas, developments and applications in all areas of advanced communication systems and networking, it covers a variety of topics, including next-generation wireless technologies such as 5G, new hardware platforms, antenna design, applications of artificial intelligence (AI), signal processing and optimization techniques. Given its scope, this book can be useful for beginners, researchers and professionals working in wireless communication and networks, and other allied fields.

Author : Antonios Drevelegas
Publisher : Springer Science & Business Media
Page : 434 pages
File Size : 53,6 Mb
Release : 2010-11-25
Category : Medical
ISBN : 9783540876502

Get Book

Imaging of Brain Tumors with Histological Correlations by Antonios Drevelegas Pdf

This volume provides a deeper understanding of the diagnosis of brain tumors by correlating radiographic imaging features with the underlying pathological abnormalities. All modern imaging modalities are used to complete a diagnostic overview of brain tumors with emphasis on recent advances in diagnostic neuroradiology. High-quality illustrations depicting common and uncommon imaging characteristics of a wide range of brain tumors are presented and analysed, drawing attention to the ways in which these characteristics reflect different aspects of pathology. Important theoretical considerations are also discussed. Since the first edition, chapters have been revised and updated and new material has been added, including detailed information on the clinical application of functional MRI and diffusion tensor imaging. Radiologists and other clinicians interested in the current diagnostic approach to brain tumors will find this book to be an invaluable and enlightening clinical tool.

Author : Mukesh Saraswat,Sarbani Roy,Chandreyee Chowdhury,Amir H. Gandomi
Publisher : Springer Nature
Page : 792 pages
File Size : 55,8 Mb
Release : 2021-11-22
Category : Technology & Engineering
ISBN : 9789811653483

Get Book

Proceedings of International Conference on Data Science and Applications by Mukesh Saraswat,Sarbani Roy,Chandreyee Chowdhury,Amir H. Gandomi Pdf

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.