Abstract
The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of medical images. The automated detection of retinal diseases not only aids ophthalmologists in making accurate clinical decisions but also enhances efficiency by saving time. This study proposes a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research, a balanced dataset was compiled by integrating data from various sources. Artificial Neural Networks (ANN) and transfer learning techniques were utilized to differentiate between healthy eyes and those affected by diabetic retinopathy, cataracts, or glaucoma. Multiple feature extraction methods were employed in conjunction with ANN for the multi-classification of retinal diseases. The results demonstrate that the model combining Artificial Neural Networks (ANN) with MobileNetV2 and DenseNet121 architectures, along with Principal Component Analysis (PCA) for feature extraction and dimensionality reduction, as well as the Discrete Wavelet Transform (DWT) algorithm, achieves highly satisfactory performance, attaining a peak accuracy of 98.2%.