Entropy-Based Feature Extraction Model for Fundus Images with Deep Learning Model

Diabetic retinopathy (DR) is stated as a disease in the eyes that affects the retina blood vessels and causes blindness. The early diagnosis and detection of the DR in patients preserve the patient's vision. In general, for the diagnosis of eye diseases, retinal fundus images are employed. The advancement in the automatic diagnosis of diseases attained higher significance for rapid advancement in computing technology in the medical field. Besides, for the diagnosis of the diseases, fundus image automatic detection is involved in the recognition of blood vessels evaluated based on the length, branching pattern, and width. However, fundus images have low contrast and it is difficult to evaluate the identification of the disease in blood vessels. As a result, it is necessary to adopt a consistent automated method to extract blood vessels in the fundus images for DR. The conventional automated localization of the macula and optic disk in the retinal fundus images needs to be improved for DR disease diagnosis. But existing methods are not sufficient for the early identification and detection of DR. This paper proposed an entropy distributed matching global and local clustering (EDMGL) for fundus images. The developed EDMGL comprises the different uncertainties for the evaluation of the classes based on local and global entropy. The fundus image local entropy is evaluated based on the spatial likelihood fuzzifier membership function estimation for segmentation. The final proposed algorithm membership function is estimated using the addition of weighted parameters through membership estimation based on the global and local entropy. The classification performance of the proposed EDMGL is evaluated based on the dice coefficient, segmentation accuracy, and partition entropy. The performance of the proposed EDMGL is comparatively examined with the conventional technique. The comparative analysis expressed that the performance of the proposed EDMGL exhibits similar to 5% improved performance in terms of accuracy, precision, recall, and F1-score.