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
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
A review of medical image data augmentation techniques for deep learning applications.
Phillip Chlap,Phillip Chlap,Hang Min,Hang Min,Nym Vandenberg,Jason Dowling,Jason Dowling,Lois Holloway,Annette Haworth +8 more
TL;DR: Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset as discussed by the authors, which has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren't typically available.
Journal ArticleDOI
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists.
Muhammad Attique Khan,Imran Ashraf,Majed Alhaisoni,Robertas Damaševičius,Robertas Damaševičius,Rafał Scherer,Amjad Rehman,Syed Ahmad Chan Bukhari +7 more
TL;DR: An automated multimodal classification method using deep learning for brain tumor type classification using two pre-trained convolutional neural network models for feature extraction and a correntropy-based joint learning approach for the selection of best features.
Posted Content
Label-Only Membership Inference Attacks
TL;DR: Label-only membership inference attacks as mentioned in this paper evaluate the robustness of a model's predicted labels under perturbations to obtain a fine-grained membership signal, and empirically show that label-only attacks perform on par with prior attacks that required access to model confidences.
Journal ArticleDOI
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
TL;DR: This survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations, and investigates the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation.
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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