# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 6 Automated Classification of DR Lesions by Using Deep Features

Table 5: Hand-crafted feature techniques used for DR detection. DL-based Densenet-264 with chimp optimization algorithm is utilized for feature extraction then these features are passes as input from SNN for the classification Of DR stages. This methodology is applied on Messidor dataset and obtained 99.73% of accuracy [151]. The DRNet is applied for classification with SVM [152]. For the categorization of DR lesions, the DAG network based on multi-feature fusion is proposed. The method is evaluated using the local dataset and DIARETDB1, and it achieves the accuracy of 98.70% and 98.50%, respectively [153]. The features are retrieved using the firefly Optimization (FFO) technique and optimization is done by iGWO to classify the DR lesions [154]. The DRNet is applied for classification with SVM [155]. The EyeNet and DenseNet (E-DenseNet) model is presented for DR classification [156]. VGGNet is employed to extract deep features from fundus images, and a transfer learning strategy is applied to increase classification performance [157]. Faster-RCNN with DenseNet-65 is utilized for the localization and categorization of multiclass DR lesions [48; 158]. For better results of the classification of DR lesions first, the features are extracted from modified deep networks like Vgg19, ResNet101, InceptionV3 and selection from these features is done by four filter-based feature selection methods namely MRMR, ReliefF, and F-test then passes these features from SVM classifier [159]. DFTSA-Net model is presented for the detection of DR lesions, in which four pretrained deep-networks like GoogLeNet, SqueezeNet, ResNet-50, and Inception-v3 are utilized as feature extractors [160]. The ConvNet model are presented for deep feature extraction from retinal images for DR lesions identification. The proposed approach has experimented on APTOS 2019 dataset with an accuracy of 97.41% [161; 162; 163; 164]. The DL approach name faster-RCNN are employed to retrieve features from fundus scans and classification of DR lesions is done by Softmax. The methodology is applied on two publicly datasets like DIARETDB1 and Messidor and get 95% accuracy [165]. The proposed CNN model utilized three pre-trained models namely VGG-16, SqueezeNet, and AlexNet as the classifier for classifying DR lesions. The model is assessed on Messidor dataset and achieved an accuracy of 98.15% [166]. The methodology is proposed that comprises of five deep CNN models are utilized for the identification of DR lesions [167]. AlexNet is used as a feature extractor, where feature reduction is done by PCA and Bow. At last, the classification of featured is done by SVM [168]. The DNN model is presented for the detection of DR, in which AlexNet is utilized for extracted features from retinal images and feature selection is done by PCA and LDA then passes these features from SVM classifier. The model is estimated on Kaggle dataset and accuracy of 97.93% [169]. The Residual network is applied for retrieving deep features and these extracted features are passed from the decision tree model to classify multiclass DR lesions [170]. A major goal of feature selection is to minimize the size of the feature space through dimensionality reduction while keeping important information preserved by choosing meaningful features. Better feature selection produces good classification results because DR classification performance relies on selected features [159]. In literature, much amount of work is done for the selection of prominent features and eliminate the noisy features using PCA, firefly algorithm [171], LDA [169], GA [172], PSO [173], wrapper-based methods [174], GWO [175], and FSAE [176]. The overview of deep features techniques is mentioned in Table 6.