scholarly journals Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Vinayak Joshi ◽  
Carla Agurto ◽  
Simon Barriga ◽  
Sheila Nemeth ◽  
Peter Soliz ◽  
...  
Keyword(s):  
Cerebral Malaria ◽  
Automated Detection ◽  
Fundus Images ◽  
Malarial Retinopathy

scholarly journals Using malarial retinopathy to improve the classification of children with cerebral malaria

2008 ◽  
Vol 102 (11) ◽  
pp. 1089-1094 ◽  
Author(s):  
Susan Lewallen ◽  
Rachel N. Bronzan ◽  
Nicholas A. Beare ◽  
Simon P. Harding ◽  
Malcolm E. Molyneux ◽  
...  
Keyword(s):  
Cerebral Malaria ◽  
Malarial Retinopathy

Automated detection of diabetic retinopathy in blurred digital fundus images

2012 ◽  
Author(s):  
Eman M. Shahin ◽  
Taha E. Taha ◽  
W. Al-Nuaimy ◽  
S. El Rabaie ◽  
Osama F. Zahran ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Detection ◽  
Fundus Images

scholarly journals A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

2013 ◽  
Vol 46 (10) ◽  
pp. 2740-2753 ◽  
Author(s):  
Meysam Tavakoli ◽  
Reza Pourreza Shahri ◽  
Hamidreza Pourreza ◽  
Alireza Mehdizadeh ◽  
Touka Banaee ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Fluorescein Angiography ◽  
Automated Detection ◽  
Fundus Images ◽  
Complementary Method

scholarly journals Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

2018 ◽  
Vol 11 (3) ◽  
pp. 1083
Author(s):  
Jeyapriya J ◽  
K S Umadevi ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Automated Detection ◽  
Convolution Neural Network ◽  
Retinal Images ◽  
Fundus Images ◽  
Catching Up

The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.

scholarly journals Intelligent Automated Detection of Microaneurysms in Fundus Images Using Feature-Set Tuning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 65187-65196
Author(s):  
Imran Usman ◽  
Khaled A. Almejalli
Keyword(s):  
Automated Detection ◽  
Fundus Images
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