An Optimized Convolutional Neural Network Based Ensemble Classification and Regression Framework for Classifying the Stages of Diabetic Retinopathy

Abstract

Deep learning (DL) techniques provide optimized solutions in a wide range of newlineapplications, such as natural language processing, face recognition, speech recognition, image analysis, and much more. Deep learning progresses from machine learning models, where the learning data is associated with task-based methods. Deep learning is identified as an effective way to handle complex image representation. Recently, the insights gained from deep learning techniques have aided the healthcare industry, especially in the medical imaging sector. Medical imaging is one of the high-priority areas for potential research with computer-aided medical devices, especially for disease diagnosis, disease monitoring newlineand treatment. Internal organs such as the brain, retina, lungs, abdomen, kidneys, and newlinemuch more can be captured in detail using medical imaging technology. newlineThis study focuses on exploring retinal disorders, which aids ophthalmologists in newlineidentifying the stages of diabetic retinopathy disease. Diabetic Retinopathy (DR) is an eye disease that affects the vision of a diabetic patient and can lead to blindness in its advanced stages. The rising number of diabetic patients worldwide is a necessity for emerging techniques in the present era. Scanning the retinal image to analyze the blood vessel layers at the rear of the eye is performed in retinal biometrics. The seepage on blood newlinevessels in the retina in diabetic patients is the cause of permanent blindness. A digital newlinephotograph of a retina is used for screening patients with DR and Glaucoma diseases. newlineDeep learning models aid in the classification of retinal images, providing optimized solutions. newlineThe objective of this study is to improve the classification performance of diabetic newlineretinopathy stages using an optimized convolutional neural network-based ensemble newlineclassification and regression framework. A deep learning technique, Convolutional Neural newlineNetworks (CNNs), is employed in the form of pre-trained resnet-34 for DR stage newlineclassification.