A Novel Fusion Deep Learning Approach for Retinal Disease Diagnosis Enhanced by Web Application Predictive Tool

Abstract

Retinal disorders such as age-related macular degeneration and diabetic macular edema can lead to permanent blindness. Optical coherence tomography (OCT) enables professionals to observe cross-sections of the retina, which aids in diagnosis. Manually analyzing images is time-consuming, difficult, and prone to mistakes. In the dynamic and constantly evolving domain of artificial intelligence (AI) and medical imaging, our research represents a significant development in the field of retinal diagnostics. In this study, we introduced "RetiNet", an advanced hybrid model that is derived from the best features of ResNet50 and DenseNet121. To the model, we utilized an open-source retinal dataset that underwent a meticulous refinement process using a series of preprocessing techniques. The techniques involved Histogram Equalization for the purpose of achieving optimal contrast, Gaussian blur to mitigate noise, morphological operations to facilitate precise feature extraction, and Data Balancing to ensure impartial model training. These operations led to the attainment of a test accuracy of 98.50% by RetiNet, surpassing the performance standard set by existing models. A web application has been developed with the purpose of disease prediction, providing doctors with assistance in their diagnostic procedures. Through the development of RetiNet, our research not only transforms the accuracy of retinal diagnostics but also introduces an innovative combination of deep learning and application-oriented solutions. This innovation brings in a novel era characterized by improving reliability and efficiency in the field of medical diagnostics.