## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Images** & **Number of Images in Use** & **Methods** & **Results** \\ \hline
[18] & KMC & DR: 170 & Feature & Overall Acc = 0.9941 \\  & & Normal: 170 & Trac & Overall Se = 0.9941 \\  & & & transform & Overall Sp = 0.9941 \\ \hline  & MESSIDOR & (DR: 170) & & \\  & & (Normal: 170) & & Overall Se = 1.0 \\  & & & Overall Sp = 1.0 \\  & & & Overall Acc = 1.0 \\ \hline
[19] & ARIA & AMD: 60 & Random transform, & Overall Acc = 0.8509 \\  & & Normal: 101 & EMD, HOS & \\  & & & nonlinear, entropy, & \\  & & & LSDA, MRMR & \\  & KMC & Normal: 270 & & Overall Acc = 0.9167 \\  & & AMD: 270 & & \\ \hline  & STARE & AMD: 47 & & Overall Acc = 1.0 \\  & & Normal: 36 & & \\ \hline
[20] & KMC & Normal: 400 & Bi-dimensional & Overall Acc = 0.8863 \\  & & Abnormal: 400 & decomposition & Overall Se = 0.8625 \\  & & (115 AMD, 170 DR, and 115 Glaucoma) & and empirical mode & Overall Sp = 0.9100 \\ \hline
[4] & KMC & Normal: 244 & Features & Overall Acc = 0.9480 \\  & & Glaucoma: 244 & Variation mode & \\  & & decomposition entropies & & \\ \hline
[16] & KMC & Normal: 404 & Continuous wavelet & Overall Acc = 0.9248 \\  & & Abnormal: 1082 & of oriented transform & Overall Se = 0.8937 \\  & & (AMD: 381, DR: 195, and Glaucoma: 506 ) & and entropy’s features & Overall Sp = 0.9558 \\ \hline
[21] & KMC & Normal: 404 & Pyramid histogram & Overall Acc = 96.21 \\  & & Abnormal: 1400 & of oriented & Overall Se = 0.9050 \\  & & (AMD: 529, DR: 365, and Glaucoma: 506 ) & and SURF features & Overall Sp = 0.9742 \\  & & & Canonical correlation analysis & \\  & & Particle swarm optimization & \\ \hline
[22] & KMC & Normal: 404 & PHOW-GMM & Overall Acc = 0.9679 \\  & & AMD: 529 & & Overall Se = 0.9673 \\  & & DR: 356 & and Fisher vector & Overall Sp = 0.9696 \\  & & Glaucoma: 506 & Canonical correlation analysis & \\  & & Particle swarm optimization & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected methods for retinal CAD systems through retinal fundus images. the public being automated retinal image analysis (ARIA) and structured analysis of the retina (STARE). The system exhibits an accuracy of 85.09%, 91.67%, and 100% for the ARIA, private, and STARE datasets, respectively. Aside from the fact that the system is dedicated to only AMD diagnosis, it is only valid when the retinal image resolution is uniform. [PERSON] et al. suggest an automatic retinal system to recognize the five severity levels of DR: normal, mild NDPR, moderate NDPR, severe NDPR, and PDR [24]. This system was developed using deep visual features (DVFs) and was tested on fundus images from three public datasets and one private. All the systems mentioned above are applied to diagnose only a single disease in the image dataset. Next, we will review work that is directed at multi-disease diagnosis DR, AMD, and glaucoma. A system dedicated to identifying DR, AMD, and glaucoma is presented by [20]. The system is based on decomposing the fundus image into 2\(D\) intrinsic mode functions (IMFs) to detect the pixels' morphological variations. The system leverages an SVM classifier to differentiate between pathological symptoms pertaining to DR, AMD, and glaucoma and normal images. The system performance achieved 88.63%, 86.25%, and 91.00% for accuracy, sensitivity, and specificity, respectively. Although this system considers three diseases, the final decision output is a binary classification (normal or abnormal). [PERSON] et al. [16] present a two-class CAD system dedicated to identifying DR, AMD, glaucoma from fundus images. In total, 404 normal and 1082 abnormal fundus images were experimented on. The system achieved 92.48%, 89.37%, and 95.58% for overall accuracy, overall sensitivity, and overall specificity, respectively, using 15 features. Although the system uses four classes of images (AMD, DR, glaucoma, and normal), the system only predicts binary classification (normal or abnormal) for each disease.