Classification of Fundus Images Based on Severity Utilizing SURF Features from the Enhanced Green and Value Planes

EasyChair Preprint 8238

Abstract

Diabetic retinopathy (DR) is a complication of long standing diabetes that affects vision. In DR the small blood vessels that supply the nutrients and oxygen to the retina get damaged which can blur the vision. If not treated in the preliminary stages DR can even lead to a complete loss of vision. Hence, a concise technique of detecting and grading the severity level of DR is necessary. The current paper focuses on an automated DR detection and severity classification technique on retinal fundus images using the Support Vector Machine (SVM) classifier. The proposed system involved pre-processing the fundus images and merging the enhanced green and value color planes. The Average Grey Value Extraction (AGVE) algorithm was applied to the merged image to extract the important information from the eye. Then, the Speeded-Up Robust Features (SURF) was used to extract the strongest feature points in the fundus image. The SVM was trained to classify the DR fundus images into various levels of severity. The experimentation results on the DIARETDB1 dataset obtained an average accuracy of 98.68% and an average F1 score of 0.99. The novel red score was found out to be a good indicator of severity. By combining the enhanced green color plane and value color plane of the HSV image, the features extracted by SURF were more accurate for predicting the severity. Thus, the proposed system will assist the doctors to detect the severity level of DR efficiently and reliably thereby enabling them to start medication in time.

Keyphrases: Diabetic Retinopathy, Severity, Support Vector Machine

@booklet{EasyChair:8238,
  author    = {Minal Hardas and Sumit Mathur and Anand Bhaskar},
  title     = {Classification of Fundus Images Based on Severity Utilizing SURF Features from the Enhanced Green and Value Planes},
  howpublished = {EasyChair Preprint 8238},
  year      = {EasyChair, 2022}}