###### Abstract  
Diabetes' serious complication, diabetic retinopathy (DR), which can potentially be life-threatening, might result in vision loss in certain situations. Although it has no symptoms in the early stages, this illness is regarded as one of the \"silent diseases\" that go unnoticed. The fact that various datasets have varied retinal features is one of the significant difficulties in this field of study. This information impacts the models created for this purpose. This study's method can efficiently learn and classify DR from three diverse datasets. Four models based on transfer learning Convolution Neural Network (CNN)--Visual Geometry Group (VGG) 16, Inception version 3 (InceptionV3), Dense Network (DenseNet) 121, and Mobile Network version 2 (MobileNetV2)--are employed in this work, with evaluation parameters, including loss, accuracy, recall, precision, and specificity. The models are also tested by combining the images from the three datasets. The DenseNet121 model performs better with 98.97% accuracy on the combined image set. The study concludes that combining multiple datasets improves performance compared to individual datasets. The obtained model can be utilized globally to accommodate more tests that clinics perform for diabetic patients to prevent DR. It helps health workers refer patients to ophthalmologists before DR becomes serious. convolutional neural network; deep learning; diabetic retinopathy; image classification; medical imaging; transfer learning +
Footnote †: journal: Medical Informatics  
0000-0001-9993-3  
medical professionals to meet the demand for ophthalmologists and not enough screening equipment due to the rise in diabetes patients [5]. Additionally, patients with early-stage DR do not show explicit symptoms or have vision problems. Due to excessive blood sugar levels, DR is a secondary illness or consequence of diabetes that damages a diabetic person's eyes. Identification of diabetic patients with retinopathy helps to avoid vision loss. Retinopathy can be avoided with early identification and a healthy lifestyle. Through techniques like image processing and deep learning models, artificial intelligence detects DR [6; 7; 8]. Proliferative (PrDR) and non-proliferative (NPrDR) retinopathy are the two stages of DR grading. The retina is healthy without DR, and there are three levels of NPrDR: mild, moderate, and severe [6; 9]. Transfer learning applies knowledge from one problem to a similar one. It allows a pre-trained model to apply information from a massive quantity of labeled data to a similar task and then fine-tune it to the target task using relatively small amounts of labeled data [10]. Collecting labeled data is both time-consuming and expensive in various practical applications. Transfer learning can solve this problem by starting with a pre-trained model that is already familiar with the core patterns and features of the data. It enables satisfactory results with a substantially smaller amount of labeled data than traditional methods require [11]. This work aims to detect DR from fundus images. It uses three publicly accessible datasets. Four pre-trained models based on CNN--MobileNetV2, VGG16, InceptionV3, and DenseNet121--have been used in this work, with several evaluation parameters, including loss, accuracy, recall, precision, and specificity. Each dataset is implemented with the same transfer learning methods, and the results are analyzed. Finally, the three datasets are combined to form a single data set and are tested on the models. The main objective of this work is as follows:  
* To evaluate the performance of models built using MobileNetV2, DenseNet121, VGG16, and InceptionV3 on retinal fundus images. * To train and test a model using three publicly available datasets and integrate it into a new dataset.