|
|
 |
| |
|
|
|
Investigation on Retinal Fundus Images for Detection of Diabetic Retinopathy and Classification Using ANFIS |
|
|
|
PP: 973-978 |
|
|
|
doi:10.18576/amis/130610
|
|
|
|
Author(s) |
|
|
|
R. Jayanthi,
K. Bommannaraja,
|
|
|
|
Abstract |
|
|
| In this paper, a novel method is developed to establish a
framework for Investigation on Retinal Fundus Images for Detection of
Diabetic Retinopathy and Classification. The preprocessing is done through
green-channel enhancement followed by top-hat filtering method to
enhance image details for subsequent segmentation and feature extraction.
The extracted features are used to train the database images using
Artificial Neuro Fuzzy Interference System. The optic disc and the blood vessels
are found using a supervised segmentation algorithm, damaged area and hard and soft
exudates using Kirsch operator to extract the features for
the classification of healthy and abnormal images of Diabetic Retinopathy from
the retinal images as Proliferal Diabetic Retinopathy (PDR) or Non-proliferal
Diabetic Retinopathy (NPDR). The NPDR is further classified into mild, moderate
and severe cases based on the calculation of microaneurysms count using Local
Thresholding (LT), Local Shifted Thresholding (LST) and the count is compared
to the Global Thresholding (GT) to
provide the best classification results. Results are optimized, in terms of their
sensitivity, specificity, accuracy and $Q$ factors by calculating
the True Positive (TP), True Negative (TN), False Positive (FP) and False Negative
(FN) analysis of the test image. The images are trained with Artificial Neural
Network (ANN) and Artificial Neuro Fuzzy Interference System (ANFIS). Analyzed
results are compared and validation set is obtained for both methods. |
|
|
|
|
|
|
|
