# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 1 Introduction
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**Reference** & **Method** & **Findings** & **Shortcomings** \\ \hline [PERSON] et al. [15] & The model is based on DenseNet, ResNet50, and VGG16 models & Detection of DR lesions and the method is computationally robust. & The method may not classify microaneuryss efficiently because these are easily misclassified under the pervading presence of fluorescein \\ \hline [PERSON] et al. [28] & A Siamese-like CNN framework by employing the concept of weigh-sharing layers based on Inception V3. & The work exhibits promising results for DR prediction with kappa score of 0.829. & It may not perform well for those sample databases where paired fundus images are not available. \\ \hline [PERSON] et al. [14] & Five different frameworks of CNN named Dense169, Inception V3, Resnet50, Dense121, and Xception were employed. & To locate and classify the DR lesions into different classes according to the severity level of moles. & The method suffers from high computational cost. \\ \hline [PERSON] et al. [40] & A DL framework named DeepDR was presented for DR detection. In addition, a new database for DR labelled DR images was also introduced. & The proposed network has attained the sensitivity value of 97.5\%, along with the specificity value of 97.7\%. & The introduced model needs to be evaluated on more complex and larger dataset. \\ \hline [PERSON] et al. [41] & A DL based method was used to predict the expected DR class and assign scores to individual pixels to exhibit their relevance in each input sample. The assigned score was employed to take final classification decision. & The introduced DL framework acquired more than 90\% of sensitivity and & The evaluation performance of the presented algorithm can be improved through appropriate measures. \\ \hline [PERSON] et al. [2] & The method was based on geometrical, morphological, and orientation features. The classification was performed through SVM. & Grading and classification of DME from fundus images with an accuracy of & The detection accuracy needs further improvement. \\ \hline [PERSON] et al. [5] & The method was based on texture features and the SVM classifier. & high-risk DME detection with accuracy of 86\%. & Experiments were performed on small dataset. \\ \hline [PERSON] et al. [30] & The method was based on thresholding and regularized regression techniques. & DME risk detection with 0.90 sensitivity. & The detection performance requires improvement. \\ \hline [PERSON] et al. [31] & The presented method was composed of two-stage CNNs & The method detects regions of interest in the retinal image and then computationally complex. \\ \hline [PERSON] et al. [32] & The end-to-end Region- based Convolutional Neural Network was used for OD and OC segmentation & OD and OC segmentation with AUC of 0.901. The method is robust to & The method is computationally complex because it employs two separate RCNNs to compute the boxes of the OC and OD, respectively. \\ \hline [PERSON] et al. [37] & The localization was achieved through RCNN, while the other stage used deep CNN to classify the computed disc into healthy. & Localization and classification of glaucoma with AUC of 0.874. & The method is computationally complex because it employs two separate RCNNs to compute the boxes of the OC and OD, respectively. \\ \hline [PERSON] et al. [37] & The modified U-Net model was improved by minimizing the original U-shape structure through adding 2-dimensional convolutional layer. & Before OD segmentation, the ground-truths were generated through the GrabCut method. & The method is computationally complex as it takes two-stage framework to localize and classify the glaucoma. The performance is affected by increasing the network hierarchy as it results in losing the discriminative set of features. \\ \hline [PERSON] et al. [39] & The region-based pixel density calculation method based on Circular Hough Transform with Hough Peak Value Selection and Red Channel Super-pixel method. & The technique is robust and efficient to optic disc segmentation. & The presented technique requires less training, however, shows lower segmentation accuracy as compare latest approaches of missing ground truths. \\ \hline \hline \end{tabular}
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