# Artificial Intelligence-Driven Eye Disease Classification Model
###### Abstract  
Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual's quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier's performance. There is a demand for a practical EDC for identifying eye diseases in the earlier stages. Thus, the author intends to build an EDC model using the deep learning (DL) technique. Denoising autoencoders are used to remove the noises and artifacts from the fundus images. The single-shot detection (SSD) approach generates the key features. The whale optimization algorithm (WOA) with Levy Flight and Wavelet search strategy is followed for selecting the features. In addition, the Adam optimizer (AO) is applied to fine-tune the ShuffleNet V2 model to classify the fundus images. Two benchmark datasets, ocular disease intelligent recognition (ODIR) and EDC datasets, are utilized for performance evaluation. The proposed EDC model achieved accuracy and Kappa values of 99.1 and 96.4, and 99.4 and 96.5, in the ODIR and EDC datasets, respectively. It outperformed the recent EDC models. The findings highlight the significance of the proposed EDC model in classifying eye diseases using complex fundus images. Healthcare centers can implement the proposed model to improve their standards and serve a more significant number of patients. In the future, the proposed model can be extended to identify a comprehensive range of eye diseases. artificial intelligence; ShuffleNet V2; single shot detection; ocular diseases; machine learning +
Footnote †: journal: Medical  
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noises and artifacts from the images. It supported the classifier in obtaining better results for each class. Figure 6 presents the findings of the performance analysis. +++  
Table 5 reveals the findings of EDC generalization on the ODIR dataset. The proposed EDC outperformed the existing state-of-the-art techniques. The higher values of precision, recall, and F1-score highlight the significance of the proposed EDC model. The proposed model efficiently handled the true positives, true negatives, false positives, and false negatives. Figure 7 shows the outcomes of comparative analysis for the ODIR dataset. Likewise, Table 6 shows the outcome of the EDC generalization using the EDC dataset. The proposed EDC model obtained a superior outcome and addressed the limitations of the existing EDC. The proposed model achieved a higher Kappa value compared to the current models. The findings suggest the efficiency of the proposed multi-class classification model. Figure 8 highlights the results of the generalization of EDC on the EDC dataset. Table 7 reveals the MAD and RMSE values of the proposed EDC model. The lower value of MAD represents the importance of the suggested EDC in classifying the real-time images. It is evident that the proposed model predicted multi-class images with high accuracy and lower error rates. In addition, the RMSE value shows that the proposed EDC model can handle the outliers and produce an optimal outcome.