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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.
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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.

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Citations
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Proceedings ArticleDOI

A Survey on Various Medical Image Classification and Feature Recognition Techniques

TL;DR: In this paper , a detailed discussion of the reviews in a categorized manner to specify the achievements attained in the earlier studies and the requirements yet to fulfill in the respective fields is presented.
Journal ArticleDOI

Distance Metric-based Segmentation and Score-Level Classification for Optimized Tumor Identification in MR Images

TL;DR: In this paper , a modified decision-based Coupled Windows median filter (MDBCWMF) was used for segmentation of brain and pancreas MR images, and the image was then feature extracted using a Gray-Level Co-Occurrence Matrix (GLCM) algorithm and classified using a parameterized combination of Back Propagation Neural Network (BPNN) and Sparse Representation (SR) approaches known as Dynamic Score Level Integration (DSLI).
Journal ArticleDOI

Automated brain tumor detection using machine learning: A bibliometric review.

TL;DR: In this paper , a systematic review and bibliometric analysis included 1747 studies of automated brain tumor detection using machine learning reported in the previous 5 years (2019-2023) from 679 different sources and authored by 6632 investigators.
Proceedings ArticleDOI

Classification of Brain Tumors via Deep Learning Models

TL;DR: In this article, the most common brain tumor types; Glioma, Meningioma and Pituitary are classified using deep learning models and the aim of this study is to ease clinicians work load and have a time efficient classification system.
Proceedings ArticleDOI

CT Image Classification Based on Stacked Ensemble Of Convolutional Neural Networks

TL;DR: Wang et al. as mentioned in this paper proposed an ensemble learning to achieve synergistic improvements in model accuracy and thereby provide highly stabilized performance on diverse medical datasets, which achieved the peak accuracy of 0.99%.
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

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

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

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.

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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