# Diabetic Retinopathy Improved Detection Using Deep Learning
## 1 Introduction  
Diabetic retinopathy (DR) is a disease that affects the blood vessels present in the retina, which is damaged due to multiple alterations by a set of metabolic disorders [1]. The blood vessels present damage in their capillaries due to the loss of pericytes, which are contractile cells that wrap capillary endothelial cells in the body's venules. Excess glucose molecules cause this damage in the blood, which clump together in the vessels disrupting circulation, a process known as ischemia. These blood vessels' deterioration produces microaneurysms, which is a saccular enlargement of the venous end of a retinal capillary by the lack of blood circulation. This process leads the vessels to lose their impermeability properties, resulting in leaks, such as hemorrhages or lipid sweating [2].  
From ischemia, two major problems occur in the retina [3]. The first problem is Vascular Endothelial Growth Factor (VEGF) cytokine protein production, which generates new blood vessels from existing ones, known as necwessels. The problem with this protein lies in the growth of the retina's surface and vitreous humor's surface. Since there is no blood circulation, these areas will only grow until they burst, producing bleeding in the vitreous cavity or pulling the retina apart. That tissue expansion results only in the patient's blindness. The second problem is plasma leakage. Due to lipid sweating, a fat accumulation begins to occur, altering the macula and leading to vision loss.  
Diabetic retinopathy can be classified from the earliest to the most advanced stages once examined the retina's fundus condition. The disease presents two main categories: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) [4]. Moreover, NPDR presents three subcategories as slight, medium, and severe.  
The damage of this first category is limited and does not go beyond the retina's inner limiting membrane. DR is proliferative when the ischemia damages result in blood vessels growing beyond the retina. PDR contains early, high-risk, and advanced sublevels. DR is developed when a patient has diabetes for at least 10 years without diagnosis and unaware of it. In this regard, the DR can be prevented if it is detected early enough by health check-ups and systematic treatment of diabetes [5].  
Digital retina fundus images were used with computer vision technique [6; 7] to automatically detect DR in different stages, and more recently, by using deep learning image processing method [8]. Deep learning (DL) is a proven methodology that automatically extracts features from images processed by a layer stack of a convolutional neural network [9]. These features can determine what is present in the image, and therefore it is useful for classification purposes. DL models showed a higher capability in recognizing objects than the human eye [10]. However, this methodology still requires considerable data and computational resources to optimize the model's parameters.  
In this work, we proposed a DL model to classify retina fundus images and detect the presence of DR in its different stages. The model was optimized using transfer-learning from DenseNet121 [11] to differentiate between a healthy eyeball and a proliferated one. Our proposal was tested over two datasets using a cross-testing method checking the complexity to acquire features from each. The model achieved better validation and testing results trained over APTOS dataset with an accuracy of 81% and 59% for each phase in predicting diabetic retinopathy presence in fundus oculi images.