# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 2 Related Work  
Ophthalmologists' manual diagnosis of DR takes a long time, requires a great deal of work and is prone to disease misdiagnosis. As a result, employing a CAD system can help to avoid misdiagnosis while also saving money, time and effort. Deep learning (DL) has arisen and gained recognition in a variety of sectors during the previous decade, including medical image analysis. DL beats all other image analysis algorithms in the vast majority of scenarios by accurately detecting features from input data for classification or segmentation. DL approaches do not necessitate the extraction of hand-crafted features, but they still necessitate a large amount of data for training [3]. Machine learning, on the other hand, necessitates the extraction of handcrafted features but does not necessitate a vast amount of data for training. Machine learning approaches for DR detection must first extract the features, as described in [5]. Then, as in [6], DR lesions are extracted for classification. CNN is a DL approach for image processing that is frequently utilized [7], highly effective and successful [8]. The related work of CNN is explained in the following section. A deep learning approach was proposed for the classification and severity of DR in retinal images. ResNet 50 was used for feature maps and random forest was used for classification purposes [9]. The proposed approach was implemented on MESSIDOR 2 and EYEPACS datasets and achieved an accuracy of 96% for MESSIDOR and 75.09 for the EYEPACS datasets [10]. The proposed approach applied maximal primary curvature for extracting the blood vessels. Following that, AHE and mathematical morphology were used to improve and eradicate incorrectly segmented regions. To train the classifier for categorization, we employed a convolution neural network (CNN). CNN's classification design consisted of a combination of squeeze and excitation and bottleneck layers, as well as a convolution and pooling layer architecture for classification between the two classes. The proposed work had been implemented on the DIARETDB1 dataset and achieved an accuracy/precision of 98.7/97.2%, respectively [11]. Fully diagnosis systems outperformed manual techniques in terms of misdiagnosis, while also saving time, effort, and money according to the current study. No-DR, mild, moderate, severe and proliferative DR were the five stages of the proposed method for DR imaging. The system was made up of two TL-based models. The first model (CNN512), which entered the complete image into the CNN model, categorized it into one of five DR stages. On the DDR and APTOS Kaggle 2019 public datasets, it achieved an accuracy of 88.6% and 84.1%, respectively. On the DDR dataset, the second model used an adapted YOLOv3 model to detect and localized DR lesions, achieving a 0.216 mAP in lesion localization. Finally, both proposed structures, CNN512 and YOLOv3, were merged to recognize DR images and target DR lesions, achieving an accuracy of 89% and sensitivity of 89%, and specificity of 97.3%, respectively [12]. The HR (DenseHyper) system was developed based on a trained features layer (TF-L) and dense feature transform layer (DFT-L) for the deep residual learning (DRL) approaches to detect HR. The DenseHyper system consisted of various dense architectures that combined TF-L with a CNN for learning features from various lesions and creating specialized features using DFT-L. The performance was evaluated on DRHAGIS, DRIVE, DIARET DB, DR1 and DR2 datasets and achieved the average sensitivity/specificity/accuracy/AUC of 93/95/95/0.96% [13], respectively. A deep CNN was used in this work for the detection and classification of DR into five stages. Three convolutional layers and a fully connected layer were included in the suggested technique. As a result, the diabetic retinopathy categorization based on convolutional neural networks was more trustworthy and accurate. This saves time with regard to classification. As a result, the condition can be treated sooner, lowering the chance of vision loss.