A Distributed Gaussian Weight Function based Deep Spectral Cluster Learning and Enhanced Deep Patch Level Classifier for Detection of Diabetic Retinopathy - J. , Usha Nandhini; S. , Lakshmi Prabha (International Journal of Psychosocial Rehabilitation, 2020)
Diabetic retinopathy (DR) is an intimidating menace to vision caused by chronic diabetes mellitus through destruction of the retina's blood vessels. Being one of the major prevalent issues of blindness, it generally affects the adults. Automatic identification of DR lesions may contribute to the screening and diagnosis of this affliction in digital fundus images. The Improved Deep Instance Learning approach, utilized for DR identification in our earlier research activity, mutually recognizes attributes and classifiers from the results and enables a considerable augmentation in the identification of DR images and their internal lesions. Nevertheless, this deep instance learning process does not refine the concluding threshold, which used to acquire hard labels in this manner, resulting in poor precision and huge complication in computation as well. To solve this issue, this study suggests the Deep Spectral Cluster Learning (DGW-DSCL) based on the Distributed Gaussian Weight strategy to identify diabetic retinopathy in order to make the execution of the classifier better. The structural similarity index (SSI) criterion is previously used to assess the purity of an image. Through the novel fuzzy histogram equalization, the augmentation of equalization and variation is achieved. And then the Deep Gaussian Weight-Distributed Spectral Cluster Learning (DGW-DSCL) based on the Distributed Gaussian Weight strategy is implemented, which streamlines an embedding method through the Gaussian weight process such that the observed depictions of comparable entities/objects are clustered into the same array and distinct entities are included in varying clusters to improve the expertise of the spectral clustering. Then it is suggested to forecast the diabetic retinopathy (DR) chances by those integrated data inserted into the advanced classifier of deep patch-level, in which the classifier's weight values are modified using the Grey Wolf Optimization (IVIR-) algorithm based on improved variability iteration ratio. The simulation findings reveal that the methodology suggested has an extreme certainty contrast to the current prediction strategies.