# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 9 Challenges and Discussion  
The analysis of DR diagnosis methods shows that DL has improved DR identification procedures and advanced methodologies, but it is still an unresolved issue that requires further study. Table 8 describes the limitations of existing approaches.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref \#** & **Datasets** & **Image Resolution** & **Image Acquisition** & **Availability** & **No. of Images** & **Use** \\ \hline
[186] & ROC & \(768\times 576\) to \(1389\times 1383\) & Digital Camera with 45” FoV & Online/Free & \(100\) & MA Detection \\ \hline
[187] & HRF & \(3504\times 2336\) & Canon CR-1 Camera & Online/Free & \(45\) & Retinal Blood Vessel \\ \hline
[188] & HEI-MED & \(2196\times 1958\) & Zeiss VISUCAM Camera with 45” FoV & Online/Free & \(169\) & Exudate Detection \\ \hline
[189] & DRiDB & \(720\times 576\) & Zeiss VISUCAM Camera with 45” FoV & Online/Free & \(50\) & Exudate Detection \\ \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref \#** & **Year** & **Methods** & **Datasets** & **Results** & **Limitations** \\ \hline
[43] & 2022 & Inception-V4, Image Flipping, Image Rotation, SGD & Messidor-2 & \(96.10\%\) SP & Using high-resolution and high-quality images at the time of training increases the performance rate. \\ \hline
[152] & 2022 & DRNet, ResNeX, GAP, FC, FSL & APTOS201 & \(98.18\%\) ACC & Imbalanced and small dataset leads to overfitting and poor approximation problems. \\ \hline
[198] & 2021 & DRNet, CNN, Regression, Image Augmentation, Image Resize, Gaussian Distribution, Euclidean Distance & DRiDB, DRIVE, BRIGHT-GS, RIMONE & \(92.10\%\) ACC, \(93.0\%\) ACC, \(90.10\%\) ACC & \(9.10\%\) ACC & In some cases, DRNet fails to produce accurate outcomes for OD localization and segmentation. \\ \hline
[199] & 2021 & CAE, Image Resize, Data Augmentation, ReLU, Skip Connections & DRiSHTI-GS, RIMONE & \(96.70\%\) & The availability of a few manually annotated images limits the reliability of supervised learning systems. \\ \hline
[200] & 2020 & CNN, VGG-16, Softmax, FC Layer, ReLU, Transfer Learning & OCTA & \(90.82\%\) SP, \(83.76\%\) SF & Large number of datasets and transfer learning approaches are utilized for the training of CNN model to overcome the overfitting problem. \\ \hline
[201] & 2019 & Vessel Tree Structure, Circular Hough Transform, Sliding Windows, Weighted Colour Channels, Image Augmentation & Local Dataset & \(88.80\%\) ACC & The presence of dust particles, reflection, and flash, on the lens of the camera in retinal images, leads to inaccurate results for the detection of OD. \\ \hline
[202] & 2019 & Inception-V3, CNN, CLAHE, Image Resize, Cropping, Padding & Messidor-2 & \(93.49\%\) ACC & Multiclass classification is a challenging task if the patient dataset contains a variety of retinal disorders. \\ \hline
[190] & 2018 & CNN, SURF, Encoding, Max-Pooling, IIT, BLT, SVM & Messidor, DR1, DR2 & \(90\%\) ACC, \(93\%\) ACC, \(96\%\) ACC & Constructing DL approaches that rely on CNN with a deep architecture means the addition of a great volume of annotated images. \\ \hline \end{tabular}
\end{table}
Table 8: Limitations in existing methods.  
After the complete survey, we observed that imbalance and small datasets lead the overfitting and poor approximation problem [152]. In several research, data augmentation has been employed to address the issue of class imbalance. The availability of few manually annotated images, limited the reliability of supervised learning systems. The solution of this issue to utilize the Generative Adversarial Networks (GANs) models [199]. The amount of datasets need to train the DL systems, as deep learning needs huge amount of data is one of the drawback of its use in medical field [200]. The addition of pre-processing stage in DL model for the better identification of DR lesions [201]. Transfer learning makes the procedure of building and designing of new model considerably simpler and faster than starting from scratch to create a new CNN architecture [190].