Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models

Abstract

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)—efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101—and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images

“…Large network models such as VGG16, ResNet152, and DenseNet121 are accompanied by a large number of model parameters and computations during the training process, which makes it difficult to run on mobile devices or embedded platforms. Therefore, it is important to study lightweight CNNs, such as shuffleNet [69] , MobileNet [70] , GhostNet [71] , etc. On the basis of guaranteeing accuracy, the model parameters and computation amount are reduced to balance the performance and efficiency.…”