Cargando…
Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy
The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients (n=125 images) and 202...
Autores principales: | Nagasato, Daisuke, Tabuchi, Hitoshi, Ohsugi, Hideharu, Masumoto, Hiroki, Enno, Hiroki, Ishitobi, Naofumi, Sonobe, Tomoaki, Kameoka, Masahiro, Niki, Masanori, Hayashi, Ken, Mitamura, Yoshinori |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236766/ https://www.ncbi.nlm.nih.gov/pubmed/30515316 http://dx.doi.org/10.1155/2018/1875431 |
Ejemplares similares
-
Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images
por: Masumoto, Hiroki, et al.
Publicado: (2019) -
Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes
por: Nagasawa, Toshihiko, et al.
Publicado: (2018) -
Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning
por: Nagasato, Daisuke, et al.
Publicado: (2019) -
Accuracy of deep learning, a machine-learning technology, using ultra–wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment
por: Ohsugi, Hideharu, et al.
Publicado: (2017) -
Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography
por: Sogawa, Takahiro, et al.
Publicado: (2020)