# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 5 Hand-Crafted Feature Extraction  
Feature extraction methods are used for extracting information from an image. it is the process of reducing a huge amount of raw data into smaller relevant data [92; 93]. Both the trained and handcrafted features are combined to get useful information. Based on characteristics it is distributed into two types such as deep feature extraction and handcrafted features extraction. For handcrafted features, the methods such as LBP, LTP, SIFT, SURF and HOG, and several others are utilized for DR classification [94; 95]. Three handcrafted textural features such as GLRLM, GLDM, and GLCM are utilized for the analysis of the statistical texture of retinal images [27; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129]. MAs feature extraction is done by GLCM for MAs diagnosis through fundus images. The method is estimated on the DIARETDB0 dataset and performs best with 99.90% of accuracy [130]. For the characterization of DR lesions encoded LBP(ULBPEZ) features are extracted from preprocessed images of Messidor-2 and EyePACS datasets and obtained accuracies of 97.31% and 93.86%, respectively [131]. GLCM features are utilized for the classification of DR on DIARETDB1 dataset obtain with an accuracy of 77.30% [132]. The FOS, HOG, and HOS features are utilized. In a grey-level image, HOG features are extracted from OD region. While HOS and FOS features are extracted from RGB channels to identify DR disease [133]. GLCM, GLRLM, and CRT are utilized to extract high-level texture feature through retina images for the classification of DR lesions on DIARETDB1 and Kaggle datasets with an accuracy of 97.05% and 91% respectively [134; 135]. HOG and GLCM texture features are extricating through green channel images as the classification of glaucoma. This methodology is employed on the ODIR dataset with a 99.39% of accuracy [136]. For the detection of multiclass DR lesions HOG descriptive feature is utilized for the representation of each DR image [137]. Four types of features like LBP, LTP, HOG, and DSIFT are extracted for characterize the extracted region of interest [138]. SURF and spatial LBP are utilized to effectively represent the DR lesions for the automated grading of DR [139]. HOG and canny edge detectors are utilized on Messidor-2 and EyePACS datasets for DR lesions recognition and obtained an accuracy of 97.88% and 97.01% respectively [140; 141]. For the detection of multiclass DR lesion three different types of handcrafted features like LBP, entropy based, and texture energy measurement (TEM) are extracted from retinal images. The approach is performed on DIARETDB1 dataset and achieved an accuracy of 94.30% [135; 142]. To capture the information on DR lesions such as MAs, HE, and HMs for efficient classification SURF, HOG and LBP are utilized on Kaggle dataset and obtained 97% of accuracy [143]. The texture, shape, and transfer learning-based features such as HOG, LBP, GLCM, GLRLM, morphology, tamura, seven CNN based architectures are utilized for glaucoma classification. The GLCM with CNN perform best for detection of glaucoma with an accuracy of 93.16% [144]. For the classification of glaucoma, the texture-based feature extraction is done by HOG, LBP, GLCM, GLDM and transform domain-based features extraction is done by Wavelet and Shearlet transform from retinal images. The method is performed on local dataset that consists of total 60 images, out of 60 images the 30 images have no DR and other 30 images have glaucoma in nature and obtain an accuracy of 93.61% [145]. Hand crafted features SURF and LOG are used for the classification of DR lesions. For interest point detection and localization SURF are utilized on retinal fundus images. The second-order Gaussian kernel is estimated using LOG and a box filter [146]. For the classification of DR lesions feature