Abbas Qaisar, Daadaa Yassine, Rashid Umer, Sajid Muhammad Zaheer, Ibrahim Mostafa E A
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan.
Diagnostics (Basel). 2023 Oct 17;13(20):3236. doi: 10.3390/diagnostics13203236.
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-to-end 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.
高血压性视网膜病变（HR）和糖尿病性视网膜病变（DR）是与高血压密切相关的视网膜疾病。高血压的严重程度和持续时间直接影响HR的患病率。HR的早期识别和评估对于预防失明至关重要。目前，用于检测HR和DR的计算机辅助方法有限。这些现有系统依赖于传统的机器学习方法，这需要复杂的图像处理技术，并且其应用通常受到限制。为了应对这一挑战，这项工作引入了一种名为HDR-EfficientNet的深度学习（DL）方法，旨在提供一种高效且准确的方法来识别各种与眼睛相关的疾病，包括糖尿病和高血压性视网膜病变。所提出的方法利用EfficientNet-V2网络进行专注于疾病分类的端到端训练。此外，该方法还融入了一种空间通道注意力方法，以增强其识别特定损伤区域和区分不同疾病的能力。HDR-EfficientNet模型是使用迁移学习开发的，这有助于克服样本类别不平衡的挑战并提高网络的泛化能力。在模型结构中添加了密集层以增强特征选择能力。使用一个超过36000张增强型视网膜眼底图像的大型数据集对所实现系统的性能进行评估。结果显示出有前景的准确性，曲线下面积（AUC）平均为0.98，特异性（SP）为96%，准确率（ACC）为98%，灵敏度（SE）为95%。这些发现表明所建议的HDR-EfficientNet分类器在诊断HR和DR方面的有效性。总之，HDR-EfficientNet方法提出了一种基于深度学习的方法，该方法在HR和DR的检测和分类方面提供了更高的准确性和效率，为诊断和管理这些与眼睛相关的疾病提供了有价值的支持。
