Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
Jen Hong Tan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Sulatha V. Bhandary,Kuang Chua Chua,Sobha Sivaprasad +6 more
TLDR
A convolutional neural network is developed and trained to automatically and simultaneously segment optic disc, fovea and blood vessels and can be used not just to segment blood vessels, but also optic disc and foveA with good accuracy.About:
This article is published in Journal of Computational Science.The article was published on 2017-05-01 and is currently open access. It has received 189 citations till now. The article focuses on the topics: Optic disc & Fundus (eye).read more
Citations
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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli +6 more
TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Journal ArticleDOI
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
Journal ArticleDOI
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Hamido Fujita,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam +7 more
TL;DR: A convolutional neural network algorithm is implemented for the automated detection of a normal and MI ECG beats (with noise and without noise) and can accurately detect the unknown ECG signals even with noise.
Posted Content
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Md. Zahangir Alom,Tarek M. Taha,Christopher Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +8 more
TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
Journal ArticleDOI
DUNet: A deformable network for retinal vessel segmentation
TL;DR: Wang et al. as discussed by the authors proposed Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end-to-end manner for retinal vessel segmentation.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Proceedings Article
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot,Yoshua Bengio +1 more
TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
Journal ArticleDOI
Ridge-based vessel segmentation in color images of the retina
TL;DR: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods.
Proceedings Article
Regularization of Neural Networks using DropConnect
TL;DR: This work introduces DropConnect, a generalization of Dropout, for regularizing large fully-connected layers within neural networks, and derives a bound on the generalization performance of both Dropout and DropConnect.