Umair Muhammad, Ahmad Jawad, Saidani Oumaima, Alshehri Mohammed S, Al Mazroa Alanoud, Hanif Muhammad, Ullah Rahmat, Khan Muhammad Shahbaz
Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
Cybersecurity Center, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.
Front Med (Lausanne). 2025 Jul 2;12:1596726. doi: 10.3389/fmed.2025.1596726. eCollection 2025.
Retinal diseases are among the leading causes of blindness worldwide, requiring early detection for effective treatment. Manual interpretation of ophthalmic imaging, such as optical coherence tomography (OCT), is traditionally time-consuming, prone to inconsistencies, and requires specialized expertise in ophthalmology. This study introduces OculusNet, an efficient and explainable deep learning (DL) approach for detecting retinal diseases using OCT images. The proposed method is specifically tailored for complex medical image patterns in OCTs to identify retinal disorders, such as choroidal neovascularization (CNV), diabetic macular edema (DME), and age-related macular degeneration characterized by drusen. The model benefits from Saliency Map visualization, an Explainable AI (XAI) technique, to interpret and explain how it reaches conclusions when identifying retinal disorders. Furthermore, the proposed model is deployed on a web page, allowing users to upload retinal OCT images and receive instant detection results. This deployment demonstrates significant potential for integration into ophthalmic departments, enhancing diagnostic accuracy and efficiency. In addition, to ensure an equitable comparison, a transfer learning approach has been applied to four pre-trained models: VGG19, MobileNetV2, VGG16, and DenseNet-121. Extensive evaluation reveals that the proposed OculusNet model achieves a test accuracy of 95.48% and a validation accuracy of 98.59%, outperforming all other models in comparison. Moreover, to assess the proposed model's reliability and generalizability, the Matthews Correlation Coefficient and Cohen's Kappa Coefficient have been computed, validating that the model can be applied in practical clinical settings to unseen data.
视网膜疾病是全球失明的主要原因之一，需要早期检测以进行有效治疗。传统上，对眼科成像（如光学相干断层扫描（OCT））进行人工解读既耗时，又容易出现不一致的情况，并且需要眼科方面的专业知识。本研究介绍了OculusNet，这是一种使用OCT图像检测视网膜疾病的高效且可解释的深度学习（DL）方法。所提出的方法是专门针对OCT中复杂的医学图像模式进行定制的，以识别视网膜疾病，如脉络膜新生血管（CNV）、糖尿病性黄斑水肿（DME）以及以玻璃膜疣为特征的年龄相关性黄斑变性。该模型受益于显著性图可视化这一可解释人工智能（XAI）技术，以解释其在识别视网膜疾病时是如何得出结论的。此外，所提出的模型部署在网页上，允许用户上传视网膜OCT图像并立即获得检测结果。这种部署展示了其融入眼科科室的巨大潜力，可提高诊断准确性和效率。此外，为确保公平比较，已将迁移学习方法应用于四个预训练模型：VGG19、MobileNetV2、VGG16和DenseNet - 121。广泛评估表明，所提出的OculusNet模型测试准确率达到95.48%，验证准确率达到98.59%，相比之下优于所有其他模型。此外，为评估所提出模型的可靠性和泛化能力，计算了马修斯相关系数和科恩卡帕系数，验证了该模型可应用于实际临床环境中的未见数据。
