A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

Abstract

Deep learning models are applied seamlessly across various computer vision tasks like object detection, object tracking, scene understanding and further. The application of cutting-edge deep learning (DL) models like U-Net in the classification and segmentation of medical images on different modalities has established significant results in the past few years. Ocular diseases like Diabetic Retinopathy (DR), Glaucoma, Age-Related Macular Degeneration (AMD / ARMD), Hypertensive Retina (HR), Cataract, and dry eyes can be detected at the early stages of disease onset by capturing the fundus image or the anterior image of the subject’s eye. Early detection is key to seeking early treatment and thereby preventing the disease progression, which in some cases may lead to blindness. There is a plethora of deep learning models available which have established significant results in medical image processing and specifically in ocular disease detection. A given task can be solved by using a variety of models and or a combination of them. Deep learning models can be computationally expensive and deploying them on an edge device may be a challenge. This paper provides a comprehensive report and critical evaluation of the various deep learning architectures that can be used to segment and classify ocular diseases namely Glaucoma and Hypertensive Retina on the posterior images of the eye. This review also compares the models based on complexity and edge deployability.

Development of the AI Pipeline for Corneal Opacity Detection

“…This is particularly relevant when considering the variety of available models and the potential need for a combination of models to solve a given task. The computational demands of these models may limit their practicality in certain clinical settings [ 21 ]. Lastly, while deep learning models can predict the development of diseases such as glaucoma with reasonable accuracy, they may miss certain cases, especially those with visual field abnormalities but not glaucomatous optic neuropathy.…”