Hybrid Deep Learning Framework for Glaucoma Detection Using Fundus Images

Glaucoma is a chronic eye condition that develops because intraocular pressure in the eye damages the visual nerve. One of the causes of blindness around the globe is due to it. Glaucoma does not initially cause vision loss, but if the condition worsens, it may leave a person permanently blind. Measurement of intraocular pressure, testing of the visual field, or inspection of the optical disc of fundus pictures are all methods used in the clinical setting to diagnose glaucoma. Early detection of glaucoma is crucial in reducing the risk of eye damage. VGG19, VGG19 + LSTM, Inceptionv3, and Inceptionv3 + LSTM are used to study the identification of glaucoma. ACRIMA is the dataset used, and it consists of 705 fundus images (396 glaucomatous images and 309 healthy images). The models are worked using data augmentation and K-fold cross-validation. The extracted features classify the input image as glaucomatous or healthy. The VGG19 + LSTM model performed the best out of all the models.