A deep learning-based technique for diagnosing retinal disease by using optical coherence tomography (OCT) images

The retina layer is the most complex and sensitive part of eye, disorders that affect it have a big impact on people's lives. Optical Coherence Tomography (OCT) imaging technology can be used to diagnose diseases are caused by pathological alterations in retina. importance early diagnosis management these illnesses cannot overstated. In this article, an approach based convolutional neural networks (CNN), deep learning method, presented for detection retinal from OCT images. A new CNN architecture has been developed disease classification. proposed method found accuracy rate 94% disorders. results obtained comparing network model application classification with MobileNet50 literature. evaluation parameter values models trained using 5-fold cross validation each type image dataset also submitted. clearly utilized as decision-making tool assist clinicians diagnosing clinical context its effectiveness thus far.