A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction PDF Download
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Author: Shakhawan H. Wady
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 21
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Book Description
This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor.
Author: Shakhawan H. Wady
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 21
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Book Description
This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor.
Author: Florentin Smarandache
Publisher: Elsevier
ISBN: 0323994571
Category : Computers
Languages : en
Pages : 495
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Book Description
Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies
Author: Paramartha Dutta
Publisher: Springer Nature
ISBN: 9811946760
Category : Technology & Engineering
Languages : en
Pages : 670
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Book Description
This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) held at Institute of Engineering & Management, Kolkata, India, during February 23–25, 2022. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT), and information security.
Author: Xuejun Li
Publisher: Frontiers Media SA
ISBN: 2832515517
Category : Medical
Languages : en
Pages : 116
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Book Description
Author: Fatih ÖZYURT
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 16
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Book Description
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: Mustafa Rashid Ismael
Publisher:
ISBN:
Category : Brain
Languages : en
Pages : 114
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Book Description
A brain tumor is the most common disease that affects the central nervous system (CNS), the brain, and spinal cord. It can be diagnosed using the safest and most reliable imaging modality, the Magnetic Resonance Imaging (MRI), by radiologists who may use the assistance of computer-aided diagnosis (CAD) tools. Automated diagnosis is sought because it is essential to overcome the drawbacks of the manual diagnosis, such as time and the stress of viewing MRI images for long hours, and the human error potential. Image analysis and machine learning algorithms are tools that can be used to build an intelligent CAD system capable of analyzing brain tumors and formulating a diagnosis on its own. Hence, it is essential to design a CAD system that is capable of extracting meaningful and precise information, and rendering an error-free diagnosis. Consequently, many researchers have proposed different methods to develop a CAD system to detect and classify abnormal growths in brain images. This dissertation presents a hybrid system for tumor classification from brain MRI images. The hybrid system is composed of a set of statistical-based features and deep neural networks. Segments of the MRI, from within the region of interest (ROI), are transformed into the two-dimensional Discrete Wavelet Transform and the two-dimensional Gabor filter methods. This allows the set of features to encompass all the directional information of the spatial domain tumor characteristics. A classifier system is developed using two types of neural network algorithms, Stacked Sparse Autoencoder (SSA) and Softmax Classifier. For the sparse autoencoder training, the sparsity regularization and L2-weight regularization are proposed. Sparsity regularization is used for its ability to control the firing of the neurons in the hidden layer, whereas L2-weight regularization is used for its ability to reduce the effect of overfitting. Two national brain tumor datasets were used to verify and validate the proposed system. The first dataset is a much larger dataset consisting of 3,064 slices of T1-weighted MRI with three kinds of tumors: Meningioma, Glioma, and Pituitary. The second dataset consists of 200 MRI slices with low-grade and high-grade Glioma tumors collected from the BRATS dataset. Implementation results using the first dataset achieved a total accuracy of 94.0%, and a specificity of 96.2%, 97.8%, and 97.3% for Meningioma, Glioma, and Pituitary tumors respectively. Using the second dataset, accuracy is at 98.8 %. Experimental results indicate not only that this system is effective, but also show that it outperforms the comparable methods.
Author: Mubashir Tariq
Publisher: Infinite Study
ISBN:
Category : Computers
Languages : en
Pages : 25
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Book Description
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: Janmenjoy Nayak
Publisher: Academic Press
ISBN: 0323903533
Category : Science
Languages : en
Pages : 422
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Book Description
Computational Intelligence in Cancer Diagnosis: Progress and Challenges provides insights into the current strength and weaknesses of different applications and research findings on computational intelligence in cancer research. The book improves the exchange of ideas and coherence among various computational intelligence methods and enhances the relevance and exploitation of application areas for both experienced and novice end-users. Topics discussed include neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. The book's chapters are written by international experts from both cancer research, oncology and computational sides to cover different aspects and make it comprehensible for readers with no background on informatics. - Contains updated information about advanced computational intelligence, spanning the areas of neural networks, fuzzy logic, connectionist systems, genetic algorithms, evolutionary computation, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems in diagnosing cancer diseases - Discusses several cancer types, including their detection, treatment and prevention - Presents case studies that illustrate the applications of intelligent computing in data analysis to help readers to analyze and advance their research in cancer
Author: Jyotismita Chaki
Publisher: Academic Press
ISBN: 0323983952
Category : Science
Languages : en
Pages : 260
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Book Description
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: Chee-Peng Lim
Publisher: Springer Nature
ISBN: 3031656407
Category :
Languages : en
Pages : 306
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Book Description
