Automated Decision Making ResNet Feed-Forward Neural Network based Methodology for Diabetic Retinopathy Detection

Abstract

The detection of diabetic retinopathy eye disease is a time-consuming and labor-intensive process, that necessitates an ophthalmologist to investigate, assess digital color fundus photographic images of the retina, and discover DR by the existence of lesions linked with the vascular anomalies triggered by the disease. The integration of a single type of sequential image has fewer variations among them, which does not provide more feasibility and sufficient mapping scenarios. This research proposes an automated decision-making ResNet feed-forward neural network methodology approach. The mapping techniques integrated to analyze and map missing connections of retinal arterioles, microaneurysms, venules and dot points of the fovea, cottonwool spots, the macula, the outer line of optic disc computations, and hard exudates and hemorrhages among color and back white images. Missing computations are included in the sequence of vectors, which helps identify DR stages. A total of 5672 sequential and 7231 non-sequential color fundus and blackand-white retinal images were included in the test cases. The 80 and 20 percentage rations of best and poor-quality images were integrated in testing and training and implicated the 10-ford cross-validation technique. The accuracy, sensitivity, and specificity for testing and analysing good-quality images were 98.9%, 98.7%, and 98.3%, and poor-quality images were 94.9%, 93.6%, and 93.2%, respectively.

Auto-Regressive Integrated Moving Average Threshold Influence Techniques for Stock Data Analysis

“…The parameters used for evaluation are closing price, price differences, and daily return. Another research proposed automated decision making ResNet feed-forward neural network-based methodology for the medical diagnosis of diabetic retinopathy [51]. In another research integrated with the simple, multiple linear regression models [31][32] [36] to generate a signal for SPY growth.…”

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

“…Their ability to diagnose complicated retinal diseases is efficient without a doubt, but in medical practice, using CNNs depends not only on how well they can diagnose the issues but also on how useful they are in places with limited computational resources. Not only CNN, but different variants of CNN like ResNet [4], VGG [5] and more have produced good accuracies statistically. These CNNs and their variants have a very high number of training parameters, and many layers which make it time-consuming in real-time predictions [6] and integration with the Internet-of-Medical-Things (IoMT) [7].…”