# A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform
## 2 Literature Review  
Deep learning has been employed in medical imaging for illness diagnosis. Several research papers have investigated the prediction of DR, utilizing CNN and pretrained models, including DenseNet, Visual Geometry Group (VGG), ResNet, and Inception [19]. Moreover, specific study is required to enhance the efficiency of the deep learning framework [20]. [PERSON] et al. [4] observed that a pretrained CNN model detected lesions better, with 94.4% accuracy. The researchers used principal component analysis to characterize the fundus pictures. [PERSON] and [PERSON] [21] introduced a sophisticated automated technique for identifying DR using deep learning methods, which offers a neutral alternative for detecting DR using any device. Another work by [PERSON] and [PERSON] [22] categorizes DR photos using support vector machines and random forest methods, with convolutional layers employed to extract the features. The analysis revealed high sensitivity and specificity of random forest, surpassing other approaches in performance. The surge in transfer learning over the past few years can be linked to the scarcity of supervised learning options for a diverse array of practical scenarios and the abundance of pretrained models. Several research studies have constructed DR models utilizing VGG16 [23; 24], DenseNet [25], InceptionV3 [26], and ResNet [27]. In their study, [PERSON] et al. [28] utilized transfer learning and attention approaches to classify DR and obtained an accuracy rate of 83.2%. Another paper [25] utilized a \"convolutional block attention module\" in conjunction with the APTOS dataset. The DR grading exercise was completed with an accuracy rate of 82%. [PERSON] et al. [29] employed a limited retinal dataset to refine a pre-existing CNN model using a process known as fine-tuning. Using the EyePACS and Messidor datasets, they outperformed the accuracy and sensitivity of previous DR grading techniques. The research authors [30] investigated DR classification by combining Swin with wavelet transformation. Their performance attained an accuracy rate of 82%, along with a sensitivity of 0.4626. Anattention model was used in conjunction with a pretrained DenseNet model by [PERSON] and [PERSON] to determine the severity of DR, utilizing the APTOS database [31], with an accuracy of 83.69%. A machine learning approach was suggested to determine the main causes of DR in individuals with elevated glucose levels [32]. Employing transfer learning approaches, this process isolates and organizes the features of DR into many classes. Before the classification phase, the entropy technique selects the most distinguishing attributes from a set of features. The objective of the model is to ascertain the level of severity of DR in the patient's eye, which will be valuable in precisely categorizing its severity. [PERSON] et al. [33] utilized a few-shot learning approach to develop a tool for detecting and evaluating DR. The approach employs an attention mechanism and episodic learning to train the model with limited training data. When evaluated on the APTOS dataset, the model achieves high DR classification and grading regarding accuracy and sensitivity. CNN's U-Net architecture is utilized for image segmentation applications [34]. The study by [PERSON] et al. [35] employed an asymmetric deep learning architecture utilizing U-Net architecture for DR image segmentation. Pictures from the green channel are utilized to analyze and thus improve the performance using CLAHE. The paper [36] presents a computerized system that mixes U-Net and transfer learning to diagnose DR automatically from fundus pictures. The researchers utilized the publicly accessible Kaggle DR Detection dataset and improved an Inception V3 model already trained to extract features. To partition the retina and optic disc, the collected characteristics are subsequently input into a U-Net model. Divided into segments, the pictures are further categorized into five degrees of DR severity using a multi-layer perceptron classifier.