Journal ArticleDOI
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
TLDR
A novel convolutional neural network (CNN) based multi-grade brain tumor classification system that is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.About:
This article is published in Journal of Computational Science.The article was published on 2019-01-01. It has received 471 citations till now. The article focuses on the topics: Medical imaging & Deep learning.read more
Citations
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Journal ArticleDOI
Deep belief network Assisted quadratic logit boost classifier for brain tumor detection using MR images
TL;DR: In this article , a Deep Belief Network Assisted Quadratic Logit BoostClassifier (DBNQLBC) technique is employed for increasing the accuracy with lesser error and time using classifying the brain images.
Journal ArticleDOI
Local and Deep Features Based Convolutional Neural Network Frameworks for Brain MRI Anomaly Detection
TL;DR: Three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI images anomaly detection are proposed and the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s is discussed.
Journal ArticleDOI
AlexNet‐NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz‐based data augmentation
Sreedhar Kollem,Katta Rama Linga Reddy,Ch. Rajendra Prasad,Avishek Chakraborty,J. Ajayan,S. Sreejith,Sandip Bhattacharya,L. Joseph,Ravichander Janapati +8 more
TL;DR: In this article, the authors used Lipschitz-based data augmentation on a dataset, and the output of the augmentation model was fed into a modified AlexNet that uses network-based deep transfer learning to extract features from a dataset.
Journal ArticleDOI
Proposed Approaches for Brain Tumors Detection Techniques Using Convolutional Neural Networks
TL;DR: Experimental results demonstrate the effectiveness of the proposed Convolutional Neural Network architecture model-based classification approach for brain tumor detection from Magnetic Resonance Imaging (MRI) images to assist professionals in Automated medical diagnostic services.
Journal ArticleDOI
An Attention-Guided CNN Framework for Segmentation and Grading of Glioma Using 3D MRI Scans
TL;DR: Gliomanet as discussed by the authors proposes a convolutional neural network (CNN)-based framework for non-invasive grading of tumors from 3D MRI scans, which leverages the spatial and channel attention modules to recalibrate the feature maps across the layers.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Journal ArticleDOI
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.
David N. Louis,Arie Perry,Guido Reifenberger,Andreas von Deimling,Dominique Figarella-Branger,Webster K. Cavenee,Hiroko Ohgaki,Otmar D. Wiestler,Paul Kleihues,David W. Ellison +9 more
TL;DR: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor and is hoped that it will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
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