An improved deep learning approach for automated detection of multiclass eye diseases

Context: Early detection of ophthalmic diseases, such as drusen and glaucoma, can be facilitated by analyzing changes in the retinal microvascular structure. The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. However, the complexity of these algorithms increases with the diversity of pathologies to be classified. In this study, we introduce a new lightweight algorithm based on CNNs for the classification of multiple categories of eye diseases, using discrete wavelet transforms to enhance feature extraction. Methods: The proposed approach integrates a simple CNN architecture optimized for multi-class and multi-label classification, with an emphasis on maintaining a compact model size. We improved the feature extraction phase by implementing multi-scale decomposition techniques, such as biorthogonal wavelet transforms, allowing us to capture both fine and coarse features. The developed model was evaluated using a dataset of retinal images categorized into four classes, including a composite class for less common pathologies. Results: The feature extraction based on biorthogonal wavelets enabled our model to achieve perfect values of precision, recall, and F1-score for half of the targeted classes. The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. This advancement not only enhances the accuracy of real-time diagnoses but also supports the development of sophisticated tools for the detection of a wide range of retinal pathologies, thereby improving clinical decision-making processes.