Data distribution.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models’ ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model’s performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model’s performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study’s contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

视网膜图像在眼科医生诊断各类眼部疾病中发挥着关键作用。现有大量研究致力于利用深度学习算法实现视网膜眼底图像的早期检测与及时治疗。深度学习模型可快速处理图像并即时输出预测结果，这一特性能够辅助实现快速诊断与治疗方案制定。本研究旨在借助卷积神经网络（Convolutional Neural Network, CNN），开发一种无创方法以实现眼部疾病的早期检测与及时诊疗。本研究使用的数据集为视网膜眼底多疾病图像数据集（Retinal Fundus Multi-disease Image Dataset, RFMiD），该数据集包含涵盖多种眼部疾病的多类眼底图像，具体包括媒体雾浊（Media Haze, MH）、视盘杯状凹陷（Optic Disc Cupping, ODC）、糖尿病视网膜病变（Diabetic Retinopathy, DR）以及健康眼底图像（WNL）。为优化模型性能，本研究采用了多种预处理手段，包括数据增强、图像裁剪、尺寸调整、数据集划分、图像转数组以及独热编码（one-hot encoding）。卷积神经网络可从输入的彩色眼底图像中提取相关特征，所提取的特征将用于生成预测性诊断结论。本文采用三种卷积神经网络模型开展实验。模型性能通过准确率、F1分数、召回率与精确率等统计指标进行量化评估。实验结果显示，经数据增强处理后，12层卷积神经网络搭建的框架表现优异，其验证集准确率可达89.81%，测试集准确率可达88.72%。针对增强后的数据集，20层卷积神经网络的验证集准确率为90.34%，测试集准确率为89.59%。尽管20层卷积神经网络的准确率更高，但该模型出现了过拟合现象。上述准确率结果表明，该深度学习模型已能够有效区分不同眼部疾病类别与健康眼底图像。本研究的核心贡献在于，通过分析彩色眼底图像，开发出一套可同时检测多种眼部疾病的可靠且高效的诊断系统。