# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 6 Automated Classification of DR Lesions by Using Deep Features
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref \#** & **Year** & **Methodology** &
\begin{tabular}{c} **Hand-Crafted Feature** \\ **Extraction Techniques** \\ \end{tabular} & **Datasets** & **Results** \\ \hline
[95] & 2022 & \
\begin{tabular}{c} RNN, Binary Image Extraction, Histogram \\ Equalization, Pseudo-Color Preprocessing, GLCM \\ \end{tabular} & GLCM & Messidor &
\begin{tabular}{c} ACC = 97\%, \\ SP = 99\%, \\ SF = 95\% \\ \end{tabular} \\ \hline
[96] & 2022 & \
\begin{tabular}{c} RNN, SVM, DA, GLCM, GLDM, GLRLM, PSO \\ \end{tabular} & GLCM, GLDM, GLRLM & Drive &
\begin{tabular}{c} ACC = 100\% \\ \end{tabular} \\ \hline
[130] & 2022 &
\begin{tabular}{c} PBRSO Clustering, GLCM, PSO Algorithm, ANN, \\ Fuzzy Logic (FL), Neuro-Fuzzy, Fuzzy \\ Contrast Enhancement \\ \end{tabular} & GLCM & DIARETDB0 & ACC = 99.90\% \\ \hline
[131] & 2022 &
\begin{tabular}{c} SVM, CNN, Histogram Matching, Green Channel, \\ CLAHE, Unsharp Filter, Median Filter, \\ Run-Length Encoding, LBF(ULBPE2) \\ \end{tabular} & LBF (ULBPE2) & Messidor-2, EyePACS & ACC = 97.31\%, ACC = 93.86\% \\ \hline
[132] & 2022 &
\begin{tabular}{c} GMM, K-Means, GLCM, PCA, MA, Grayscale \\ Conversion, Morphological Operations, Average \\ Histogram Equalization \\ \end{tabular} & GLCM & DIARETDB1 & ACC = 77.30\% \\ \hline
[133] & 2021 & \
\begin{tabular}{c} FOS, HOG, Decision Tree (DT), Naive \\ Bayes, KNN, Genetic Algorithm (GA) \\ \end{tabular} & FOS, HOG, HOG &
\begin{tabular}{c} High-Resolution \\ Fundus (HRF) \\ \end{tabular} & ACC = 96.67\% \\ \hline
[134] & 2021 &
\begin{tabular}{c} Sequential Minimal Optimization (SMO), GLCM, \\ GLRLM, CKT, Image Conversion, \\ Morphological Operations \\ \end{tabular} & GLCM, GLRLM, CKT & DIARETDB1, Kaggle & ACC = 97.05\%, ACC = 91\% \\ \hline
[136] & 2021 &
\begin{tabular}{c} HOG, GLCM, Green Channel, Grayscale \\ Conversion, Inception-V3, SVM, SqueezeNet, \\ Xception, DenseNet 201, ResNet50 v2 \\ \end{tabular} & HOG, GLCM & ODIR & ACC = 99.39\% \\ \hline
[137] & 2021 & \
\begin{tabular}{c} HOG, PCA, KNN, Hadoop DFS \\ \end{tabular} & HOG &
\begin{tabular}{c} DIARETDB0, \\ Messidor-2 \\ \end{tabular} & SP = 96.42\% \\ \hline
[138] & 2020 & \
\begin{tabular}{c} LBP, LTP, HOG, DSIFT, SVM, Grayscale \\ Conversion, PCA, CLAHE \\ \end{tabular} & LBP, LTP, HOG, DSIFT & Local Dataset &
\begin{tabular}{c} SF = 96.40\%, \\ SP = 96.90\% \\ \end{tabular} \\ \hline
[139] & 2020 & \
\begin{tabular}{c} SURF, Spatial LBP, CLAHE, ANN, ELM, KNN \\ \end{tabular} & SURF, Spatial LBP &
\begin{tabular}{c} Local Dataset, Kaggle, \\ DIARETDB0, \\ DIARETDB1 \\ \end{tabular} & ACC= 98.98\% \\ \hline
[140] & 2020 & \
\begin{tabular}{c} ResNet-50, Inception-V3, Canny Edge Detector, \\ HOG, Stochastic Gradient Descent (SGD) \\ \end{tabular} & HOG &
\begin{tabular}{c} MESSIDOR-2, \\ EyePACS \\ \end{tabular} & ACC = 97.01\%, \\ \hline
[141] & 2020 &
\begin{tabular}{c} CNN, Median Filter, Adaptive Histogram \\ Equalization, Otsu Method, Radial Length (RL), \\ Discrete Fourier Transformation (DFT), HOG \\ \end{tabular} & HOG & Local Dataset 1, Local & Precision = 100\%, \\ \hline
[142] & 2020 &
\begin{tabular}{c} Green Channel, CLAHE, Waterset \\ Transform, Skaher’s Transformation, \\ Gabor Filtering, LBP, TEM, Entropy, DEN, NN \\ \end{tabular} & LBP, TEM, Entropy-Based & DIARETDB1 & ACC = 94.30\% \\ \hline
[143] & 2019 & \
\begin{tabular}{c} SURF, HOG, Box Filters, \\ K-Means Clustering ANN, SVM \\ \end{tabular} & SURF, LOG & Messidor &
\begin{tabular}{c} SF = 95.92\%, \\ SP = 98.90\% \\ \end{tabular} \\ \hline
[144] & 2019 &
\begin{tabular}{c} CNN/Seg\_8, CNN-Vgg, CNNVgg-f, \\ CNN-CaffeNet, GLRLM, GLCM, HOG, LBP, \\ Morphology, SVM, MLP, Random Forest \\ \end{tabular} & GLCM, LBP, HOG & HRF, JSIE, ACRIMA & ACC = 98.10\%, \\ \hline
[145] & 2019 &
\begin{tabular}{c} SVM, KNN, Green Channel, CLAHE, Wavelet \\ Transform, Shear’s Transform, HOG, LBP, \\ GLCM, GLDM \\ \end{tabular} & HOG, LBP, GLCM, GLDM & Local Dataset & ACC = 93.61\% \\ \hline
[147] & 2018 & \
\begin{tabular}{c} Bag-of-Words (BoW), SVM, SURF, \\ Radial Basis Function (RBF) \\ \end{tabular} & SURF & Messidor &
\begin{tabular}{c} ACC = 94\% \\ SP = 91\% \\ SP = 93\% \\ \end{tabular} \\ \hline
[148] & 2017 &
\begin{tabular}{c} HOG, LBP, Decision Tree (DT), \\ Random Forest (RF), SVM \\ \end{tabular} & HOG, LBP & Local Dataset & ACC = 95.31\% \\ \hline
[142] & 2016 &
\begin{tabular}{c} SURF, LBP, HOG, SVM, CNN, Logistic \\ Regression, Random Forest, Crogan and Resize, \\ Green Channel, CLAHE, Median Filter \\ \end{tabular} & SURF, LBP, HOG & Kaggle & ACC = 97\% \\ \hline \hline \end{tabular}
\end{table}