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
More filters
Journal ArticleDOI
Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation
TL;DR: Wang et al. as discussed by the authors proposed a multi-input unet model based on the integrated block and the aggregation connection to achieve efficient and accurate segmentation of tumor structure, which can effectively improve memory efficiency.
Journal ArticleDOI
Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.
TL;DR: In this article, a reconstruction independent component analysis (RICA) feature extraction method was proposed to detect multi-class brain tumor types (pituitary, meningioma, and glioma).
Journal ArticleDOI
MSF-Model: Multi-Scale Feature Fusion-Based Domain Adaptive Model for Breast Cancer Classification of Histopathology Images
TL;DR: In this paper , a multi-scale feature fusion-based domain adaptive model for breast cancer classification using histopathology images is proposed, which has two blocks and six lightweight sub-models where each block contains three models.
Journal ArticleDOI
Extreme Learning Bat Algorithm in Brain Tumor Classification
TL;DR: In this article , a novel approach in feature selection using bat algorithm with Extreme Learning Machine (ELM) and for enhancing the accurate classification by Transfer Learning (BA + ELM-TL).
Journal ArticleDOI
Multimodal brain tumor detection using multimodal deep transfer learning
TL;DR: In this paper , the authors proposed a new multimodal deep transfer learning for MRI brain image segmentation, where the knowledge transfer between and within modalities is considered to tackle the challenge of having different distributions between the training and test sets.
References
More filters
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.
Related Papers (5)
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more