Journal ArticleDOI
Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms
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TLDR
The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images and can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.About:
This article is published in Biocybernetics and Biomedical Engineering.The article was published on 2019-01-01. It has received 307 citations till now. The article focuses on the topics: Convolutional neural network.read more
Citations
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Journal ArticleDOI
Multi-Classification of Brain Tumor Images Using Deep Neural Network
TL;DR: A DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets and the results indicate the ability of the model for brain tumor multi-classification purposes.
Journal ArticleDOI
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
TL;DR: A new CNN architecture for brain tumor classification of three tumor types is presented, simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images and two databases.
Journal ArticleDOI
Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images
TL;DR: A deep neural network is first pre-trained as a discriminator in a generative adversarial network on different datasets of MR images to extract robust features and to learn the structure of MR pictures in its convolutional layers.
Journal ArticleDOI
Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019
TL;DR: This paper provides a systematic literature survey of techniques for brain tumor segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques, metaheuristic techniques and hybridization of these two.
Journal ArticleDOI
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
TL;DR: In this paper, the authors proposed a method for brain tumor classification using an ensemble of deep features and machine learning classifiers, where the top three deep features which perform well on several machine-learning classifiers are selected and concatenated as an ensemble-of-deep features which is then fed into several machine learning classes to predict the final output.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Journal ArticleDOI
The 2007 WHO Classification of Tumours of the Central Nervous System
David N. Louis,Hiroko Ohgaki,Otmar D. Wiestler,Webster K. Cavenee,Peter C. Burger,Anne Jouvet,Bernd W. Scheithauer,Paul Kleihues +7 more
TL;DR: The fourth edition of the World Health Organization (WHO) classification of tumours of the central nervous system, published in 2007, lists several new entities, including angiocentric glioma, papillary glioneuronal tumour, rosette-forming glioneurs tumour of the fourth ventricle, Papillary tumourof the pineal region, pituicytoma and spindle cell oncocytoma of the adenohypophysis.