Journal ArticleDOI
Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
Jen Hong Tan,Hamido Fujita,Sobha Sivaprasad,Sulatha V. Bhandary,A. Krishna Rao,Kuang Chua Chua,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +8 more
TLDR
It is shown that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy, and that the net achieved a sensitivity of 0.7158 for exudates and dark lesions on the CLEOPATRA database.About:
This article is published in Information Sciences.The article was published on 2017-12-01. It has received 221 citations till now. The article focuses on the topics: Convolutional neural network.read more
Citations
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Journal ArticleDOI
Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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.
Journal ArticleDOI
Automated EEG-based screening of depression using deep convolutional neural network.
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Hojjat Adeli,D. P. Subha +7 more
TL;DR: It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
Journal Article
Automatic detection of microaneurysms in color fundus images for screening of diabetic retinopathy.
TL;DR: This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease.
Journal ArticleDOI
Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening
TL;DR: A new dataset named DDR is provided for assessing deep learning models and further exploring the clinical applications, particularly for lesion recognition, indicating that lesion segmentation and detection are quite challenging.
References
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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.
Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Posted Content
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.