# Artificial Intelligence-Driven Eye Disease Classification Model
## 6 Conclusions  
In this study, the authors addressed the limitations of the existing EDC. A DL-based EDC was proposed to identify eye diseases from complex retinal images. The proposed image pre-processing approach produced high-quality images. The denoising autoencoders were employed to generate the images using encoder and decoder techniques. The authors applied the SSD approach for feature extraction and employed WOA with the Levy Flight and Wavelet mutation strategies to select critical features. In addition, an optimized ShuffleNet V2 model was employed for EDC. The author generalized the proposed EDC model on the ODIR and EDC datasets. The findings of generalization highlight that there is a significant improvement in the proposed model. The proposed model obtained optimal accuracy values of 99.1 and 99.4, and sensitivity of 98.9 and 98.7, for the ODIR and EDC datasets, respectively. It outperformed the recent models using the learning rate of \(1\times 10^{-4}\), parameters of 23.5 m and 27.4 m, FLOPs of 0.78 G and 0.68 G, and testing times of 1.08 s and 1.15 s for the ODIR and EDC datasets. The findings indicate that the proposed EDC can be implemented in healthcare centers with minimal hardware and software requirements. The experimental outcome suggests that the model can be deployed in healthcare centers across the globe. The proposed EDC has significant practical implications and real-world applications in ophthalmology and healthcare. It can potentially aid in the early detection of a range of ocular conditions, including DR, cataracts, and glaucoma. Screening programs for the general public can benefit from adopting the proposed EDC. The proposed model is beneficial in areas with limited expert eye care, as it enables the screening of huge populations at minimal cost. It can play a role in identifying potential drug candidates and assessing the effectiveness of new treatments for eye diseases. Implementing the proposed EDC can enhance research endeavors by facilitating the analysis of extensive datasets. However, the proposed model encountered a few limitations in classifying the fundus images. The highly imbalanced datasets caused challenges in pre-processing the fundus images. There is a demand for compelling image pre-processing methods to overcome image noises and artifacts. In the future, the authors will focus on improving the performance of the proposed EDC with additional eye diseases. The ensembling technique can be employed to improve the model's performance.  
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. 4257).  
Not applicable.  
Data Availability Statement: ODIR Dataset. Available online: [[https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k](https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k)]([https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k](https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k)) (accessed on 5 January 2023). EDC Dataset. Available online: [[https://www.kaggle.com/datasets/gunavenkatododdi/eye-diseases-classification](https://www.kaggle.com/datasets/gunavenkatododdi/eye-diseases-classification)]([https://www.kaggle.com/datasets/gunavenkatododdi/eye-diseases-classification](https://www.kaggle.com/datasets/gunavenkatododdi/eye-diseases-classification)) (accessed on 1 January 2023).