Cargando…
Microaneurysm detection in fundus images using a two-step convolutional neural network
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the...
Autores principales: | Eftekhari, Noushin, Pourreza, Hamid-Reza, Masoudi, Mojtaba, Ghiasi-Shirazi, Kamaledin, Saeedi, Ehsan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542103/ https://www.ncbi.nlm.nih.gov/pubmed/31142335 http://dx.doi.org/10.1186/s12938-019-0675-9 |
Ejemplares similares
-
Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism
por: Zhang, Lizong, et al.
Publicado: (2019) -
A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism
por: Masoudi, Mojtaba, et al.
Publicado: (2018) -
Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms
por: Khojasteh, Parham, et al.
Publicado: (2018) -
Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method
por: Raudonis, Vidas, et al.
Publicado: (2023) -
Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning
por: Almasi, Ramin, et al.
Publicado: (2022)