Designing and adding a useful module to the CNN backbones is used to address the imbalanced DR grading problem [13]. It is accomplished using self-supervised learning to train a model for retinal disease diagnosis using multimodal data. These multimodal data consist of raw and transformed fundus images, as well as synthesized fundus fluorescein angiography (FFA) modality data generated by a GAN model [21]. The small network modules are comprised of a selected number of the layers to learn salient features from the data [22]. The layers include convolution, batch normalization, ReLU, and max pooling [22]. The deep-feature generator is designed by the non-fixed size patch division model [23]. Since most DR datasets lack sufficient data or have an imbalanced distribution between classes, several approaches are considered to deal with this problem. These approaches include using transfer learning to exploit information of the models pre-trained on large-scale datasets by applying augmentation techniques or generating synthetic data through GANs to increase the diversity of the data [4], and developing a self-training deep neural network model to utilize unlabeled data [24]. [PERSON] et al. proposed a brain tumor segmentation method based on the fusion of a semantic segmentation module, an edge detection module, and a feature fusion module. This method of fusion outperforms several state-of-the-art brain tumor segmentation methods [25]. Modern deep learning algorithms have produced unprecedented achievements for object recognition recently. Large-scale datasets are one of the main factors determining the success of deep-learning-based models when performing visual tasks [26]. This demonstrates that more training data ensures that the learning algorithm can obtain more meaningful and discriminating representations from the data. The models depend on training data, and the more data, the better the results, as large amounts of data avoids overfitting and enables the development of more sophisticated and robust models [27]. However, unlike image data from nature, medical data are not readily available, especially images from samples medically identified as abnormal, because the average number of patients with a given disease is much lower than the number of healthy individuals. On the other hand, annotation is also a factor in the difficulty of obtaining sufficient medical image data. As a result, a shortage of sufficient data and interclass imbalanced data distribution often arise in the medical field of the real world [28; 29]. Unfortunately, neural networks suffer greatly from imbalanced learning due to the class-imbalanced data distribution, and they often suffer from overfitting due to the lack of sufficient medical data [30; 31]. This challenging situation also exists in DR image data. Due to the relatively fewer data samples with DR symptoms in the dataset available for training a model, the model is biased to represent the majority category without DR symptoms, leading to false-negative predictions of models. For a medical diagnosis, a false-negative prediction is more serious and dangerous to patients than a false-positive diagnosis because it ignores the disease [32]. Therefore, importance should be attached to this biased classification problem caused by the skewed distribution of training data. However, there are currently very few studies dedicated to DR detection with imbalanced learning. This paper is focused on learning DR detection from such an interclass imbalanced fundus image dataset. The purpose of this paper is to propose methods to overcome this biased DR detection in order to reduce the error diagnosis rate and improve the performance of the DR detection system. The principle of supervised learning is to learn to make decisions in the direction of given supervision signals related to target tasks. The lack of sufficient labeled data limits the generalization ability of supervised learning.