Title : Application of deep learning techniques for the detection of ocular health: Challenges and opportunities
Abstract:
Recent developments in artificial intelligence (AI), in particular machine learning (ML) and Deep Learning (DL), have demonstrated promising outcomes in various fields, including the internet of things, automated machinery, and healthcare. DL techniques are dominating medical image analysis for the early detection of ophthalmic diseases namely diabetic retinopathy, glaucoma, and age-related macular degeneration. This study discusses the overall potential of ML and DL techniques to automatically grade, recognize, and assess the abnormal features that will empower ophthalmologists to provide accurate diagnoses and facilitate personalized health care. In this study, a brief overview of the analysis of traditional and advanced techniques was discussed. Further, this study highlights the images of transfer learning techniques on the early detection and diagnosis of ocular health. Publically available datasets such as digital retinal images for vessel extraction (DRIVE), high resolution fundus (HRF), and one public dataset are used to validate the proposed system in terms of accuracy, precision, specificity and F1-score. This study may help clinicians to early progression with high accuracy for timely interventions and utilization of advanced DL technology, with a specific focus on its applications in ocular imaging techniques.
