A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.

ABSTRACT: Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state. Also the diagnosis of diseases did not employ machine learning methods to categorize candidate fundus images into true positive and true negative ratio. Several candidate fundus images did not include more detailed feature selection technique for diabetic retinopathy. To apply machine learning methods and classify the candidate fundus images on the basis of sliding wind

SUBMITTER: Krishnamoorthy S 

PROVIDER: S-EPMC4431725 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature