An Iterative Constraint Spectral Model for Ophthalmic Disease Detection Using Transfer Learning

Because the eye is among the body’s most vital organs, maintaining its health and diagnosing it is vital. With the advancement of digital technology, several new deep learning approaches are being applied to swiftly diagnose eye problems. Using CNN & transfer learning to detect eye disorders A supervised learning algorithm & a novel loss function were used to automatically diagnose eye disorders using retinal fundus color pictures and a library of known diseases. The model is compared to an ophthalmology dataset. Deep learning is a vital part of ML. The capacity of DL can automatically locate, identify, and grade problematic aspects in eye disorders will soon allow ophthalmologists could make accurate diagnosis and deliver customized healthcare. Nowadays, many patients undergo several scans to diagnose. Most of them become tired of waiting for scan results. Our algorithm saves doctors time by automating the tedious task of manually screening each patient. Decreased time spent on procedures also helps people take the tests. During this time, the efficiency of the deep learning model with recommended loss function is compared to the existing datasets. Retinal fundus color pictures are the most often utilized ophthalmic data to assist clinicians diagnose eye problems. The retina is the tissue at the rear of the eye that lets us see. The optic disc, macula, and blood vessel Finally, pre-trained CNN and SVM classifiers categorize the ocular illnesses.