## 2 Related Work  
In recent history, several methods have been proposed for DR detection. [PERSON] et al. [2] presented a deep learning and dimensionality reduction-based method for DR classification. The paper experiments with three pre-trained CNN feature extractors--ResNet50, ResNet152, and SqueezeNet1--to extract useful features from retinal images. The extracted features might contain some irrelevant or redundant ones, which are refined via principal component analysis (PCA) and passed into a machine learning classifier for final classification. [PERSON] [26] proposed a feature extraction module that deteriorates the original feature space of a CNN into content and style representation spaces. Spatial attention and texture attention are integrated into the content and style features, respectively. Later, both streams are fused before the final classification. The proposed module achieves 98% accuracy on binary classification and 85% accuracy on multi-class classification on the APTOS dataset. [PERSON] et al. [27] leveraged dense blocks and transition blocks to construct a custom feature extractor for automatic DR detection. The dense block consists of densely connected convolutional layers, where features extracted from previous blocks are propagated to the subsequent blocks. The transition block, on the other hand, consists of point-wise convolution and 2 \(\times\) 2 max-pooling. This layer compressesthe output of the dense block by reducing the number of features and dimensionality. Finally, the extracted features are passed through a convolutional block attention module (CBAM) [28], where the features are refined with channel attention and spatial attention. This paper also experiments with the APTOS dataset and attains 97% and 82% accuracy on binary class and multi-class classification, respectively. [PERSON] et al. [29] balanced APTOS19 and DIARETDB1 datasets using generative adversarial network (GAN). For classification, they present an ensemble learning method consisting of two state-of-the-art CNN architectures: DenseNet101 and ResNext. [PERSON] et al. [30] presented a fusion model made of two visual geometry group (VGG) networks and a custom deep convolutional network to achieve 90% accuracy on the APTOS dataset. Nonetheless, due to the integration of VGG networks, which are recognized for their high parameter numbers, the proposed system becomes infeasible in real-world applications for modest computational platforms. [PERSON] et al. [31] performed an investigation on three high-performing image classifiers, termed VGG16, ResNet50, and DenseNet169. To conduct the experiment, they exploited a public dataset (APTOS) and a private dataset (Mauritian) that the authors collected from local hospitals. Given that APTOS is an imbalanced dataset, the authors performed two data augmentation methods--flipping and brightness adjustment--on the training set to balance the dataset, which resulted in an improvement in the classification performance. Among the three models, ResNet50 produced the best classification accuracy. On the contrary, in separate research [32], [PERSON] et al. found that ResNet50 is the most overfitted model among the 26 state-of-the-art deep learning models. According to the experiments, EfficientNetB4 is the most optimal model for DR classification, followed by Inception-ResNet-v2 and NASNetLarge in the second and third positions, respectively. [PERSON] [33] employed four pre-trained CNN models to extract spatial features. In the next stage, redundant features were deleted and passed to the feature integration phase. Finally, the system classified fundus images with the help of three state-of-the-art machine learning classifiers. Although the model achieves a noteworthy performance in retinopathy of prematurity (ROP) classification, the integration of four classifiers in the feature extraction phase makes the system heavy. [PERSON] et al. [34] extracted features from DR images using two pre-trained CNN feature extractors: ResNet50 and DenseNet121.