Diabetic retinopathy classification based on dense connectivity and asymmetric convolutional neural network - Cao, Juan; Chen, Jiaran; Zhang, Xinying; Peng, Yang (Neural computing & applications, 2025)
Diabetic retinopathy (DR) is the leading cause of blindness in diabetics. The low contrast and microscopic nature of the lesions lead to a high false positive rate for automated DR screening. To address this issue, we propose a neural network named AC-DenseNet for the five-stage DR classification. In order to exploit the shallow features and enhance the feature extraction performance, dense connectivity is added to the convolution layer of AC-DenseNet. For the convolution layer to be more robust for DR detection in rotated or flipped pictures, asymmetric convolution branches are also introduced. In addition, attention mechanisms and auxiliary classifiers are incorporated into the network for the improvement of the performance of DR classification. We validate AC-DenseNet on the enhanced Kaggle dataset. The results show that AC-DenseNet can achieve 88.8% accuracy, 97.1% specificity, and 88.7% sensitivity, demonstrating that our model outperforms several state-of-the-art algorithms.