Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely
associated with high blood pressure. The severity and duration of hypertension directly impact
the prevalence of HR. The early identification and assessment of HR are crucial to preventing
blindness. Currently, limited computer-aided methods are available for detecting HR and DR. These
existing systems rely on traditional machine learning approaches, which require complex image
processing techniques and are often limited in their application. To address this challenge, this
work introduces a deep learning (DL) method called HDR-EfficientNet, which aims to provide an
efficient and accurate approach to identifying various eye-related disorders, including diabetes and
hypertensive retinopathy. The proposed method utilizes an EfficientNet-V2 network for end-toend
training focused on disease classification. Additionally, a spatial-channel attention method
is incorporated into the approach to enhance its ability to identify specific areas of damage and
differentiate between different illnesses. The HDR-EfficientNet model is developed using transfer
learning, which helps overcome the challenge of imbalanced sample classes and improves the
network’s generalization. Dense layers are added to the model structure to enhance the feature
selection capacity. The performance of the implemented system is evaluated using a large dataset of
over 36,000 augmented retinal fundus images. The results demonstrate promising accuracy, with an
average area under the curve (AUC) of 0.98, a specificity (SP) of 96%, an accuracy (ACC) of 98%, and
a sensitivity (SE) of 95%. These findings indicate the effectiveness of the suggested HDR-EfficientNet
classifier in diagnosing HR and DR. In summary, the HDR-EfficientNet method presents a DL-based
approach that offers improved accuracy and efficiency for the detection and classification of HR and
DR, providing valuable support in diagnosing and managing these eye-related conditions.