In recent years, deep learning has emerged as a transformative tool, offering automated, efficient, and reliable methods for both the detection and classification of cataract severity.

It employs support vector machines (SVM) and logistic regression. The machine learning model is trained using the four extracted characteristics, which are the cup area, disc area, CDR, and rim width.

A timely diagnosis of cataracts can help prevent vision loss and other disease-related complications. Several recent developments in machine learning have significantly impacted medical science.

This project leverages the power of Generative Adversarial Networks (GANs) to significantly improve accuracy in glaucoma detection. Through the integration of external datasets and innovative Deep ...

Glaucoma is an eye condition that causes the retina to slowly deteriorate over time. If the disease is detected early enough, its progression can be stopped. Unfortunately, early diagnosis is rare ...

MINNEAPOLIS — A deep learning model trained on fundus photographs showed promise in the detection of severe glaucoma, with lower accuracy in mild to moderate cases, according to a poster ...

Objective: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields. Design: Algorithm development for ...

Over at the IBM Blog, Rahil Garnavi writes that IBM researchers have developed new techniques in deep learning that could help unlock earlier glaucoma detection. "Earlier detection of glaucoma is ...

Future of AI in Ophthalmology This study represents a major step toward AI-assisted ophthalmology, proving that multi-model deep learning can significantly enhance early glaucoma detection. However, ...