CAM-HR: Clinical Attention Enhanced CNNs for Hypertensive Retinopathy Classification

Hypertensive retinopathy (HR) is a retinal vascular disorder caused by long-term hypertension and can lead to severe visual impairment. If not detected early, it could progress to irreversible visual impairment and even blindness. Recent advances in deep learning have enabled automated analysis of retinal images to support clinical diagnosis. In this study, we propose a Clinical Attention Module–enhanced convolutional neural network framework (CAM-HR) for automatic classification of HR stages from Optical Coherence Tomography (OCT) images. In the initial scenario, various state-of-the-art architectures of convolutional neural networks (CNN) have been utilized as baseline models. In the next scenario, the Clinical Attention Module (CAM) is utilized with these architectures to focus on clinically significant regions of the retina, such as vascular structures and lesion locations. The models are evaluated using accuracy, precision, recall, F1-score, and Cohen’s kappa metrics. Experimental results demonstrate that the proposed CAM module consistently improves classification performance across different backbone architectures, achieving the best performance with the ConvNeXt + CAM model. These findings indicate that clinically guided attention mechanisms can significantly enhance automated HR diagnosis from OCT images.