# Artificial Intelligence-Driven Eye Disease Classification Model
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**ODIR**} & \multicolumn{2}{c}{**EDC**} \\  & **CI** & **SD** & **CI** & **SD** \\ \hline Proposed EDC & [97.8–98.3] & 0.0021 & [98.1–98.3] & 0.0019 \\ [PERSON] et al., model [39] & [95.3–96.1] & 0.0023 & [96.7–97.1] & 0.0021 \\ [PERSON] et al., model [35] & [96.4–96.8] & 0.0027 & [97.4–97.8] & 0.0024 \\ [PERSON] et al., model [37] & [97.1–97.6] & 0.0026 & [96.4–97.1] & 0.0019 \\ [PERSON] et al., model [38] & [96.9–97.4] & 0.0020 & [97.9–98.1] & 0.0017 \\ [PERSON] et al., model [40] & [97.1–97.5] & 0.0024 & [96.1–96.3] & 0.0022 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Findings of uncertainty analysis. pre-processing technique to improve the image quality. Moreover, the proposed model achieved a superior accuracy compared to the [PERSON] et al. model. [PERSON] et al. [37] developed a deep neural network model for detecting cataracts from fundus images. They optimized the network using AO. Similarly, the proposed EDC model applied AO for tuning the hyper-parameters of the ShuffleNet V2 model. However, the proposed EDC model outperformed the [PERSON] et al. model. [PERSON] et al. [38] followed a hierarchical DL approach for glaucoma diagnosis. They employed cup-to-disk ratio vectors for the image classification. The larger number of vectors reduced the performance of the classifier. On the other hand, the author followed the feature extraction and selection approaches for generating the feature sets and improved the overall prediction accuracy of the proposed EDC model. [PERSON] et al. [39] proposed DL-based ophthalmic disease detection. They used focal loss and correntrophy-induced loss function to overcome the data imbalances of the complex datasets. In contrast, the author employed data augmentation and compelling image pre-processing approaches to address data bias. [PERSON] et al. [40] employed principal component analysis for feature extraction. They used MobileNet and DenseNet 121 models to fuse the features to classify the images. On the other hand, the authors employed the lightweight version of the ShuffleNet model and obtained exceptional prediction accuracy. The authors addressed the challenges in classifying the fundus images. The existing studies required substantial computational resources to obtain a reasonable outcome. In contrast, the proposed research achieved an exceptional outcome with limited resources. The generalization of the proposed method was performed on ODIR and EDC datasets, respectively. The findings reveal the significance of the proposed model in EDC. The proposed model can be implemented in remote places. In addition, it can be developed as a mobile application and assist clinicians in treating patients. The proposed feature extraction and EDC play a significant role in the automation of diagnosing and evaluating different eye conditions. The feature extraction process discovers and extracts pertinent information from fundus images for classifying ocular diseases. It minimizes data dimensionality while maintaining essential data. The extracted feature enables the machine learning model to prioritize necessary data, consequently enhancing the manageability of the categorization operation. The suggested EDC detects eye diseases, including diabetic retinopathy, glaucoma, and cataracts. The proposed model can identify particular patterns or irregularities within the eye, such as detecting exudates or hemorrhages. In healthcare settings, these methods may improve the speed and precision of diagnosing and monitoring eye diseases. The proposed model has a substantial impact on EDC. It was trained to recognize critical patterns, structures, and anomalies in retinal or fundus images to classify eye diseases. It can capture hierarchical and abstract characteristics by employing several layers of convolution, pooling, and non-linear activation functions. The basic features, including edges, textures, and shapes, were utilized to train the proposed model.