Advancing Retinal Image Analysis for Neurodegenerative Disease Prediction and Detection Using Advanced Deep Learning Techniques

Retinal lesion disease encompasses various pathological states impacting the retina, marked by atypical alterations or injury to tissue, frequently resulting in vision impairment or loss. Two obstacles in progressing retinal image analysis for predicting and detecting neurodegenerative diseases using sophisticated deep learning methods include dealing with variations in data, which makes it challenging to create universally applicable models due to disparities in image quality, patient characteristics, and disease manifestations, and addressing interpretability and validation concerns, where the absence of clear model interpretation and the complexities of real-world validation impede the comprehension and practical use of deep learning predictions. To address the mentioned constraints, a strategy involving the amalgamation of VGG16, ResNet50, and InceptionV3 models is utilized for analyzing the retinal image dataset. The ensemble method incorporating VGG16, ResNet50, and InceptionV3 models enhances predictive accuracy and robustness through synergizing the unique capabilities of each architecture, resulting in superior system performance overall. VGG16 demonstrates remarkable training accuracy of 100% by the fourth epoch, while ResNet50 and InceptionV3 achieve peak training accuracies of 100% and 92.86%, respectively. However, validation accuracies fluctuate, with VGG16 and ResNet50 maintaining accuracies of 50% and $\mathbf{2 5 \%}$, respectively, whereas InceptionV3 maintains consistent performance with a validation accuracy of 50%.