Computer Aided Diagnostic System for Diabetic Retinopathy Detection using Image Processing and Artificial Intelligence

The number of individuals who develop Diabetic
Retinopathy (DR) has increased significantly in recent years. Early
detection and diagnosis is essential to prevent the vision loss.
Ophthalmologist need to analyze mass retinal images to discover the
anomalies, for example, spilling veins, retinal swelling(macular edema),
greasy stores on the retina (exudates), and changes in the veins. Early
detection of DR from retinal images is a challenging task. Medical
image examination is the most effective method for diagnosis of DR.
Computer Aided Diagnosis (CAD) systems, which can be used in
clinical environments assists an ophthalmologist in diagnosing and
detecting DR. This paper aims to investigate, the state of art regarding
CAD for DR. The review focus on major techniques in image
processing and data mining that are employed for developing a CAD
system for DR. This survey also comes up with a common analysis of
the current CAD system according to the employed modalities for DR
diagnosis or detection. Future research works are discussed to develop
efficient CAD systems for DR diagnosis or detection.

Index Terms— Computer Aided Detection, Classification,
Diabetic Retinopathy, Feature Extraction, Image Processing,
Preprocessing.