Implementation of Intelligent System for Retina-Based Eye Diseases

Abstract

To prevent vision loss and encourage prompt intervention, it is essential to identify ocular illnesses such cataract, retinal conditions, and glaucoma as early as possible. The detection and diagnosis of various eye disorders have showed promise when using machine learning approaches. This study provides a thorough analysis of current developments in the use of machine learning algorithms for the diagnosis of glaucoma, retinal disorders, and cataracts.

First, we go over the various imaging techniques, such as optical coherence tomography (OCT), fundus photography, and slit lamp biomicroscopy, that are frequently used to identify ocular diseases. We investigate how key pathogenic elements and biomarkers connected to glaucoma, retinal disorders, and cataract are captured by various imaging modalities.

Then, we give an overview of several machine learning techniques and how they might be used to identify eye diseases. These algorithms include deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), as well as supervised learning techniques like support vector machines (SVMs) and random forests. We go over each algorithm's benefits, drawbacks, and appropriateness for various ocular disease detection jobs. We also examine the datasets that can be utilised to train and test machine learning models for detecting eye diseases. To ensure the robustness and generalizability of the created models, we emphasise the necessity of large, diverse datasets.

The accuracy, sensitivity, specificity, and area under the curve (AUC) of machine learning models for the identification of ocular diseases are also examined in this research. We talk about the difficulties in assessing these models and suggest new possibilities for performance evaluation. Finally, we go over the practical ramifications of eye disease detection using machine learning, such as early diagnosis, individualised treatment planning, and resource allocation. We also discuss the moral issues and potential drawbacks of applying machine learning techniques in therapeutic settings.

Ultimately, this review offers a thorough summary of the state-of-the-art in machine learning-based cataract, retinal disease, and glaucoma diagnosis. It demonstrates how these methods could fundamentally alter how ocular diseases are diagnosed and treated while also highlighting significant obstacles and promising research topics.