# Diabetic Retinopathy Improved Detection Using Deep Learning
## 2 Related Works  
The earliest digital retina fundus images were classified using hand-engineering extracted features with empirical obtained parameters. One of these works was presented by [PERSON] et al. [12]. The authors have proven that computer vision techniques were suitable to automatically detect microaneurysm. Their experiments relied on simplistic morphological and thresholding techniques using eight features among pixel area and total pixel intensity, measured on each candidate. The proposed method achieved results comparable to those obtained by clinicians, proving that automated microaneurysm detection can be used for diagnostic purposes. In more recent works, the methods were evolved to detect not only microaneurysm in the fundus images, but also the stage of diabetic retinopathy. [PERSON] et al. [7] proposed a method to classify the retina fundus images as normal, moderate, severe, and proliferative DR. The input images were preprocessed using morphological operations with disc and diamond structuring elements. After that, six features were obtained considering the perimeter and area of the pixels in the RGB channels. For classification, a single-layer feed-forward neural network with 8 units in the hidden layer was used. As input, 6 units were used for each feature value mentioned above and 4 units as output, one for each severity of DR. [PERSON] et al. [6] proposed a method implementing an image preprocessing with adaptive histogram equalization to be further processed by morphological operations and texture analysis to extract features about blood vessels. A multilayer perceptron processed the extracted features. The architecture consisted of 4 inputs considering the blood vessel area and perimeter, the exudate area, and texture. Inputs were processed by 2 hidden layers with 8 units each before the exit with 2 units to classify the for the normal retina represented by '01', the nonproliferative DR represented by '10', and the proliferative DR represented by '11'. [PERSON] et al. [13] proposed the recognition of a microaneurysm, using computer vision techniques to preprocess the image. Firstly, the nonuniform illumination was reduced, and the grayscale intensities were normalized to obtain two features. The first features used principal component analysis to discriminate the round-shaped candidates' region, and the second one used radon features to count the number set of discrete angle values. Those features were then passed to a hierarchical system of classifiers composed of two perceptron units to learn the threshold required to determine if there is a microaneurysm or not in the region. Hand-crafted extracted features require specialized knowledge and empirical results to achieve accurate microaneurysm detection in digital images. In this regard, recent image processing advances were automated the feature acquisition stage from raw images to useful information using convolutional neural networks [14; 15]. Many classification tasks use deep learning methods with a large stack of convolutional layers to acquire features from the network's input. One first approach was presented by [PERSON] et al. [16], where the authors automate the DR screening. The validation results obtained an AUC of 0.95 in the Messidor dataset using a 5-fold cross-validation technique. Another work proposed by [PERSON] et al. [17], presented a multilayer perceptron outperforming a convolutional neural network with 83.6%, using extracted statistical features such as average, median, standard deviation, maximum, and minimum. A computer-aided DR diagnosis system was proposed by [PERSON] [18] processing the image with background subtraction to further be processed by the AlexNet model and obtain features. These features are then processed by a super vector machine (SVM) algorithm using a 10-fold cross-validation technique. The results showed that the use of feature reduction before SVM processing improves the results reaching a 97.93% of validation accuracy. [PERSON] et al.