Diabetic retinopathy detection and classification using hybrid feature set.

Abstract:

:Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E-ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

journal_name

Microsc Res Tech

authors

Amin J,Sharif M,Rehman A,Raza M,Mufti MR

doi

10.1002/jemt.23063

subject

Has Abstract

pub_date

2018-09-01 00:00:00

pages

990-996

issue

9

eissn

1059-910X

issn

1097-0029

journal_volume

81

pub_type

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