Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture

Abstract

Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy.

HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture

“…As mentioned, these anomalies are indicative of damage to essential parts of the eye and may escalate to HR if they are not caught in time. In addition, whereas DR-related eye illness cannot be reversed, HR-related eye disease may be [11]. Some retinal disorders, including eye conditions connected to HR, can be diagnosed by ophthalmologists using computer-aided diagnostic (CAD) systems.…”

“…Feature extraction and object identification are areas where previous systems have heavily used deep learning models (DLMs) [8][9][10][11]. A unique active deep learning-based convolutional neural network (CNN) was developed to deal with the challenges of multilayered network design training.…”

RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy

“…Hemorrhage size increases considerably with definite beading at severe NPDR levels. PDR is the stage where blindness occurs due to neovascularization that causes blood leakage [4]. A microaneurysm is a blood vessel growth in the retina where veins appear as a red patch with a sharp edge on the retinal layer.…”

“…Hemorrhage size increases considerably with definite beading at severe NPDR levels. PDR is the stage where blindness occurs due to neovascularization that causes blood leakage [4]. Table 1 presents symptoms for each classification level of DR.…”

“…Most of the time, colored fundus images from optical coherence tomography (OCT) and fundus fluorescein angiography (FFA) are used to inspect and screen for clinical DR. OCT fundus images proved to be a more effective and efficient way of diagnosing and screening because of their ease of availability and their high resolution. The provision of pathological variations can easily be obtained through FFA [4]. However, this It is critical to address this, as if it remains undiagnosed for a long period of time, it might lead to major consequences such as blindness and vision loss.…”