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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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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.

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.

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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Journal ArticleDOI

Early fire detection using convolutional neural networks during surveillance for effective disaster management

TL;DR: An early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments is proposed and an adaptive prioritization mechanism for cameras in the surveillance system is proposed to ensure the autonomous response.
Journal ArticleDOI

Magnetic Resonance Scans Should Replace Biopsies for the Diagnosis of Diffuse Brain Stem Gliomas

TL;DR: This study reviewed the neuroradiology and neurosurgery reports as well as the pathological specimens of children entered on the study to determine the effectiveness of hyperfractionated radiation for the treatment of children and young adults with brain stem gliomas.
Journal ArticleDOI

A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

TL;DR: A new model to optimize virtual machines selection in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0 applications is proposed and outperforms on the state-of-the-art models in total execution time and the system efficiency.
Journal ArticleDOI

Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling

TL;DR: The goal of this study is to provide a new computer-vision based technique to detect Alzheimer's disease in an efficient way using convolutional neural network and increased the classification accuracy by approximately 5% compared to state-of-the-art methods.
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