LW-CNN-based extraction with optimized encoder-decoder model for detection of diabetic retinopathy

Abstract

In the field of computer vision, automatic diabetic retinopathy (D.R.) screening is a well-established topic of study. It’s tough since the retinal vessels are hardly distinguishable from the backdrop in the fundus picture, and the structure is complicated. To learn data representations at numerous levels of abstraction, deep learning (DL) allows for the development of computational representations with several processing layers. Small, inconspicuous lesions generated by the disorder are hard to detect since they are tucked away beneath the eye’s structure. In this research, a lightweight convolutional neural network (LW-CNN) was used to extract structures from images of blood vessels, and different preprocessing methods were employed. The features are extracted, and then D.R. is classified using the suggested learning technique, which includes an encoder, dense branch. Effective categorization relies on the usage of multi-scale information collected from various nodes in the network. Grasshopper’s optimisation algorithm (GHOA) is used to fine-tune the recommended classifier’s hyper-parameters. The DIARETDB1 benchmark dataset is assessed using 80% training data and 20% testing data to get a diagnosis of the disease’s severity. The proposed model improved D.R. image classification with accuracy of 0.992 for DIARETDB1 database and 0.981 for APTOS 2019 blindness detection dataset. The state-of-the-art models for D.R. dataset images only achieved less accuracy and precision as compared with the proposed model.