The traits learned by pre-training on the large dataset can be transferred to the new network, where only the classification component is trained on the new smaller dataset, to fine-tune the new data [7]. TL reduces the amount of time spent constructing and training a deep CNN model as well as the computing resources needed. The visual geometry group (VGG) [8], inception modules (GoogleNet) [9], residual neural network (ResNet) [10] and neural architecture search network (NasNetLarge) [10] are examples of the many high-performing pre-trained models found in the literature. In 2017, [PERSON] et al. [11] applied a pre-trained Inception V3 model on the Eye-PACS fundus dataset and achieved an accuracy of 48.2%. Meanwhile, [PERSON] et al. [12] investigated the use of transfer learning for identifying DR by comparing several network topologies, such as AlexNet, VGG-S, VGG16 and VGG19, to two datasets: the Messidor and DR1 datasets. With an area under the curve (AUC) of 98.34%, the VGG-S architecture scored the best AUC for the Messidor dataset while an AUC score of 97.86% was obtained for the DR1 dataset. Similarly, in 2019, using the EYE-PACS dataset, [PERSON] et al. [13] proposed a deep All-CNN architecture for DR classification. The model obtained an accuracy of 86.64%, a loss of 0.46 and an average F1 score of 0.6318. Meanwhile, using the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset [14], [PERSON] et al. [15] described a classification method using a modified Xception architecture model, which is an extension of the Inception architecture, on the dataset and obtained an accuracy of 83.09%, a sensitivity of 88.24% and a specificity of 87.00%. [PERSON] et al. [16] implemented transfer learning using four pre-trained models, namely AlexNet, Res-Net18, SqueezeNet and GoogleNet. AlexNet obtained the highest accuracy of 97.9%. In [PERSON] et al. [17], a pre-trained Inception V3 model was applied to a subset of the APTOS dataset for DR classification, and the accuracy was 90.9% and the loss was 3.94%. [PERSON] et al. [18] presented a method incorporating the ExtraTree classifier, which is a popular ensemble learning algorithm based on decision trees and bagging learning techniques, and achieved a classification accuracy of 91%. In 2020, [PERSON] et al. [19] proposed a modified version of the VGG-19 that achieved an accuracy of 88%-89% when both 5-fold, and 10-fold cross validation methods were used, respectively. Using the same APTOS 2019 BD dataset, [PERSON] et al. [20] achieved a classification accuracy of 90% using a pre-trained Dense169 model. Before they trained the images, the latter were pre-processed by removing the black border and applying Gaussian blur filter. Moreover, [PERSON] et al. [21] fine-tuned a pre-trained VGG16 model for classifying the severity of DR. An average class accuracy of 74%, sensitivity of 80%, specificity of 65% and AUC of 0.80 were achieved. [PERSON] et al. [22] developed a novel deep learning hybrid model with pre-trained Inception-ResNet-v2 as a base model and it obtained a test accuracy of 72.33%on Messidor-1 and 82.18% on the APTOS dataset. On the other hand, [PERSON] et al. [23] used a deep learning model based on the ResNet architecture to classify fundus images into five different classes. Images were obtained from the Shanghai Integrated Diabetes Prevention and Care System study. Firstly, the different features, such as microaneurysm, hard exudate and haemorrhage were detected, and then they concatenated the model used and the base model for DR classification. The model achieved AUCs of 0.943, 0.955, 0.960 and 0.972, for mild, moderate, severe and proliferative cases. [PERSON] et al. [24] discussed the usage of transfer learning by using a pre-trained Inception V3 on the DR dataset obtained from the VisionQuest Biomedical database. The model classified fundus images into six classes including identifying scars, and it achieved a sensitivity and specificity of 90%, with an AUC of 95%.