Model parameters.

Introduction:
Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images.
Method:
The study utilized several models, including DenseNet121, ResNet50, Inception V3, MobileNet, and OCT images obtained from the WATBORG Eye Clinic, to detect and classify multiple retinal diseases such as glaucoma, macular edema, posterior vitreous detachment (PVD), and normal eye cases. The preprocessing techniques employed included data augmentation, resizing, and one-hot encoding. We also used the Gaussian Process-based Bayesian Optimization (GPBBO) approach to fine-tune the hyperparameters. Model performance was evaluated using the F1-Score, precision, recall, and area under the curve (AUC).
Result:
All the CNN models evaluated in this study demonstrated a strong capability to detect and classify various retinal diseases with high accuracy. MobileNet achieved the highest accuracy at 96% and AUC of 0.975, closely followed by DenseNet121, which had 95% accuracy and an AUC of 0.963. Inception V3 and ResNet50, while not as high in accuracy, showed potential in specific contexts, with 83% and 79% accuracy, respectively.
Conclusion:
These results underscore the potential of advanced CNN models for diagnosing retinal diseases. With the exception of ResNet50, the other CNN models displayed accuracy levels that are comparable to other state-of-the-art deep learning models. Notably, MobileNet and DenseNet121 showed considerable promise for use in clinical settings, enabling healthcare practitioners to make rapid and accurate diagnoses of retinal diseases. Future research should focus on expanding datasets, integrating multi-modal data, exploring hybrid models, and validating these models in clinical environments to further enhance their performance and real-world applicability.

引言：
视网膜疾病是一类重要的全球公共卫生问题，常可导致严重视力损害乃至失明，引发显著的功能与社会功能受限。本研究确立了一项新颖的研究目标：开发并对比多款当前前沿的卷积神经网络（convolutional neural network, CNN）模型，以利用光学相干断层扫描（optical coherence tomography, OCT）图像实现视网膜疾病的自动化检测与分类。
方法：
本研究采用多款模型开展实验，涵盖DenseNet121、ResNet50、Inception V3及MobileNet，并使用来自WATBORG眼科诊所的OCT图像，针对青光眼、黄斑水肿、玻璃体后脱离（posterior vitreous detachment, PVD）以及正常眼部样本等多种视网膜疾病开展检测与分类任务。所采用的预处理技术包括数据增强、图像尺寸调整及独热编码。本研究同时采用基于高斯过程的贝叶斯优化（Gaussian Process-based Bayesian Optimization, GPBBO）方法对模型超参数进行微调。模型性能通过F1值（F1-Score）、精确率（precision）、召回率（recall）以及曲线下面积（area under the curve, AUC）进行评估。
结果：
本研究评估的所有CNN模型均展现出较强的各类视网膜疾病检测与分类能力，且分类精度较高。其中MobileNet取得了最高的96%分类精度与0.975的AUC值，紧随其后的DenseNet121分类精度为95%，AUC值达0.963。Inception V3与ResNet50的分类精度虽相对较低，但在特定场景下仍具备应用潜力，二者分类精度分别为83%与79%。
结论：
本研究结果证实了先进CNN模型在视网膜疾病诊断中的应用潜力。除ResNet50外，其余CNN模型的分类精度均可与其他当前前沿深度学习模型相媲美。值得注意的是，MobileNet与DenseNet121在临床场景中展现出可观的应用前景，可辅助医护人员快速且精准地完成视网膜疾病诊断。未来研究可聚焦于扩充数据集规模、融合多模态数据、探索混合模型，并在临床环境中对上述模型进行验证，以进一步提升其性能与实际应用价值。