# Artificial Intelligence-Driven Eye Disease Classification Model
## 5 Discussion  
The authors proposed a DL-based EDC for classifying the fundus images in this study. CLAHE and denoising autoencoders were employed to improve the image quality. The feature extraction was performed using the SSD technique. An improved WOA was employed to select the key features. The ShuffleNet V2 model was fine-tuned in order to classify the fundus images. In medical image processing pipelines, image pre-processing is a crucial step. The utilization of image enhancement techniques enhances the visual quality of medical images. Additionally, it plays an essential role in improving the accuracy and dependability of subsequent analysis, diagnosis, and clinical decision-making processes. The author applied the CLAHE to cover the image's maximum range. The denoising autoencoders technique helped the study to produce high-quality images using encoders and decoders. The proposed data augmentation technique increased the diversity of training data in order to improve the performance of the proposed EDC model. In feature extraction and selection, the SSD technique is used to extract features from complex images. The detection head technique has assisted the proposed model in extracting the key patterns of eye diseases. The minor spots were identified during this process. In addition, WOA, with the Levy Flight and Wavelet strategy, selected the critical features for classifying eye diseases. Furthermore, the ShuffleNet V2 model was fine-tuned using the AO algorithm. The ShuffleNet V2 architecture contains fewer convolutional layers that help the proposed model to produce an effective outcome with limited resources. Moreover, the feature extraction and selection approaches assisted the ShuffleNet V2 model in generating the results with minimal convolutional layers. Table 2 shows the significance of the proposed EDC in classifying the fundus images. The proposed feature selection process minimized the features and assisted the proposed EDC in identifying eye diseases with fewer computational resources. Due to the hyper-parameter optimization and feature selection, the proposed EDC required a lower number of parameters and FLOPs with a learning rate of \(1\times 10^{-4}\). Tables 3 and 4 outline the performance evaluation of the proposed EDC. The proposed EDC achieved an accuracy of 99.1 and 99.4 for ODIR and EDC datasets, respectively. Compared to the EDC dataset, the ODIR dataset comprises fewer images. However, the proposed EDC performed exceptionally on the EDC and ODIR datasets. Tables 5 and 6 show the findings of the comparative analysis. The proposed EDC outperformed the recent EDC models by achieving optimal accuracy and sensitivity. The proposed feature extraction and selection techniques played a significant role in improving the efficiency of the proposed model. Table 7 highlights the outcome of the loss function. The AO hyper-parameter optimization fine-tuned the shuffleNet V2 model's parameters to overcome the challenges in classifying the fundus images with fewer FLOPs and parameters. Finally, Table 8 outlines the statistical significance of the proposed EDC. The proposed EDC generated outcomes with SD values of 0.0021 and 0.0091 for the ODIR and EDC datasets. The findings outline the capability of the proposed EDC to deliver a high-standard outcome. [PERSON] et al. [35] employed a spatial correlation technique for extracting features. The technique used pixel-wise comparison for feature extraction. A dense correlation network was used for the image classification. In contrast, the author used an effective image