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Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

Ricardo A. Pizarro, Xi Cheng, Alan Barnett, Herve Lemaitre, Beth A. Verchinski, Aaron L. Goldman, Ena Xiao, Qian Luo, Karen F. Berman, Joseph H. Callicott, Daniel R. Weinberger, Venkata S. Mattay
2016 Frontiers in Neuroinformatics  
The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated  ...  SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset.  ...  This is the first study that uses a multidimensional machine-learning algorithm based on automated regional extracted features to automate quality assessment of structural magnetic resonance images (MRI  ... 
doi:10.3389/fninf.2016.00052 pmid:28066227 pmcid:PMC5165041 fatcat:5m3nfdnfjjch3gicm6ngz7jbyq

Brain Tumor Identification and Classification of MRI images using deep learning techniques

Zheshu Jia, Deyun Chen
2020 IEEE Access  
Acknowledgment This paper is funded by the National Natural Science Foundation of China (No. 60572153; No. 60972127).  ...  Algorithm 1: Extreme learning machine algorithm based on SVM Input: j,I Output: l, x For (i=0) Figure 4 : 4 Graphical Representation of SVM based on linearly separable data Figure 5 : 5 Dice Similarity  ...  In section 3, a Fully Automated Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques.  ... 
doi:10.1109/access.2020.3016319 fatcat:tkwey5fzdvdfzpbejzgpfaqzuu

Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images

2018 Tomography  
In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists.  ...  Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.  ...  AI for Artificial Brain MRI  ... 
doi:10.18383/j.tom.2018.00042 fatcat:pvckdw6af5cypfkelszocyxwwy

Systematic Review of Computational Models for Human Brain Parcellation

Patrick Owate
2017 Journal of Review and Research in Sciences  
Aim: To systematically review the existing models of parcellating brain Magnetic Resonance Images, their strengths and weaknesses, and the possibility of using them for ageing brain.  ...  Materials and Methods: PubMed was searched combining search terms for Parcellation, Brain and Magnetic Resonance Imaging (MRI).  ...  Odun of the same department for all the help in the area of administrative tasks.  ... 
doi:10.36108/jrrslasu/7102/40(0130) fatcat:op3ganrsxbgsrplclh2omedxam

Demyelinating and ischemic brain diseases: detection algorithm through regular magnetic resonance images

Darwin P. Castillo Malla, René Samaniego, María José Rodríguez-Álvarez, Yuliana Jiménez, Oscar Vivanco, Luis Cuenca, Andrew G. Tescher
2017 Applications of Digital Image Processing XL  
This work presents the advance to development of an algorithm for automatic detection of demyelinating lesions and cerebral ischemia through magnetic resonance images, which have contributed in paramount  ...  The sequences of images to be used are T1, T2, and FLAIR.  ...  It is a supervised machine learning algorithm which can be used for both classification and regression challenges 15, 18, 19 .  ... 
doi:10.1117/12.2274579 fatcat:odgwnfrhmnft5hx7w5atjfvpxy

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  Arterial Spin Labeling (ASL) MRI 11313 0N Artifact reduction in brain magnetic resonance imaging (MRI) by means of a dense residual network with K-space blending (DRN-KB) SESSION 5 REGISTRATION  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Deep into the Brain: Artificial Intelligence in Stroke Imaging

Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang
2017 Journal of Stroke  
Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial.  ...  In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.  ...  an example of automated segmentation of infarct lesions using supervised machine learning techniques.  ... 
doi:10.5853/jos.2017.02054 pmid:29037014 pmcid:PMC5647643 fatcat:sbvi7boytndjfkk7em57aopqse

A Deep Belief Network Based Brain Tumor Detection in MRI Images

2017 International Journal of Science and Research (IJSR)  
To detect brain tumor in magnetic resonance imaging many automated diagnostic systems play an important role.  ...  MRI (Magnetic Resonance Imaging) is widely used medical imaging technique used to assess tumors, but large amount of data produced by MRI may vary greatly. Thus manual detection will be a challenge.  ...  MRI (Magnetic Resonance Imaging) is widely used medical imaging technique used to assess tumors, but large amount of data produced by MRI may vary greatly. Thus manual detection will be challenging.  ... 
doi:10.21275/art20175321 fatcat:gdfqhhhy7jdcpnymeqcuezwlzm

Survey on Early Detection of Alzhiemer's Disease Using Capsule Neural Network

SHARUNYA SR, VIJAYALAKSHMI DESAI, MEENAKSHI SINGH, KUSUMA M
2020 International Journal of Artificial Intelligence  
MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD. An MRI uses magnets and powerful radio waves to create a complete view of your brain.  ...  Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI).  ...  A machine learning pipeline are used to help automate machine learning workflows and algorithms.  ... 
doi:10.36079/lamintang.ijai-0701.65 fatcat:ldtjp6spija4jk3hnsr3gyrehq

Taxonomy Of Brain Tumor Classification Techniques: A Systematic Review

Virupakshappa, Dr. Basavraj Amarapur
2017 Zenodo  
The main purpose of image processing is to improve the quality of the images for human/ machine perception.  ...  Brain image classification is very important because it provides anatomical structure information, necessary for planning of the treatment and patient follow-up.  ...  A Supervised learning algorithm joined with a pattern recognition technique was created and cross-approved in 18F-FDG PET investigations of a structure of a brain tumour implantation [51] .  ... 
doi:10.5281/zenodo.996584 fatcat:u5xf4qxodrgg3ezlmajxie5v2e

IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering

Yu-Dong Zhang, Zhengchao Dong, Juan Manuel Gorriz, Yizhang Jiang, Ming Yang, Shui-Hua Wang
2021 IEEE Access  
She is currently a Pediatric Committee Member of the Chinese Society of Radiology and the Chinese Medical Doctor Association of Radiology.  ...  She has served as a Visiting Scholar for the Department of Radiology, University of Maryland from January 2013 to June 2014.  ...  quality of an image fusion algorithm.  ... 
doi:10.1109/access.2021.3080355 fatcat:oez6u3npt5ff7aw7tscwyvlmvq

A Review of Analysis of Various Brain Tumor Detection Technique

Hardeep Kaur
2021 International Journal for Research in Applied Science and Engineering Technology  
The various classification techniques which are classified into supervised and unsupervised learning are reviewed in terms of certain parameters  ...  The development of this technology has made the detection of the mass possible using image processing methods.  ...  The quality of brain Magnetic Resonance (MR) image was enhanced and the tumor was segmented and detected more efficiently using the designed approach.  ... 
doi:10.22214/ijraset.2021.33707 fatcat:d5ybl532fbagzpejsw5dy2sk3m

Taxonomy Of Brain Tumor Classification Techniques: A Systematic Review

Virupakshappa, Dr. Basavaraj Amarapur
2017 Zenodo  
The main purpose of image processing is to improve the quality of the images for human/machine perception.  ...  Brain image classification is very important because it provides anatomical structure information, which is necessary for planning of the treatment and patient follow-up.  ...  A Supervised learning algorithm joined with a pattern recognition technique was created and cross-approved in 18F-FDG PET investigations of a structure of a brain tumour implantation [51] .  ... 
doi:10.5281/zenodo.1013807 fatcat:srcgtw7mmzdzzhwrnn3w4saigu

Review on Brain Tumor Segmentation and Classification Techniques

N S Zulpe, V P Pawar
2017 International Journal of Engineering Research and  
Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy.  ...  One more important phase in the medical sciences is Brain tumor classification, the images acquired from different modalities such as CT, MR that should be verified by the physician for the further treatment  ...  of a charged fluid to segment anatomic structures in magnetic resonance (MR) images of the brain.  ... 
doi:10.17577/ijertv6is110008 fatcat:lh6yklz5cfen7nwsgjwblalz4q

Improvement of Automatic Diagnosis of Soft Tissue Tumours Using ML

P. Charitharyan
2021 International Journal for Research in Applied Science and Engineering Technology  
Because of their scarcity and diversity, they are difficult to detect when seen using Magnetic Resonance Imaging (MRI).  ...  Because of their scarcity and diversity, they are difficult to detect when seen using Magnetic Resonance Imaging (MRI).  ...  They may be classed into supervised learning, semi-supervised learning, and unsupervised learning algorithms based on how they employ training pattern identifiers.  ... 
doi:10.22214/ijraset.2021.38981 fatcat:dhudj7jl3zawfmtbtavdhekhzy
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