Multilevel CNN for anterior chamber angle classification using AS-OCT images

Glaucoma is the second leading cause of blindness and the first leading cause of irreversible blindness. The main types of the disease are open-angle and angle-closure glaucoma. In people with angle-closure, the anterior chamber angle is narrow, which leads to a rising intraocular pressure, and consequently, optic nerve damage, causing vision loss. Since it is irreversible, an early diagnosis is essential. So, angle classification is fundamental for diagnosis. Anterior segment optical coherence tomography is one of the imaging tests used to diagnose the disease. In addition to the fact that no eye contact is necessary, this test provides a fast way to analyse the anterior chamber angle, to classify it as open or closed. We propose the anterior chamber angle classification method based on visual feature extraction, using deep neural networks in this work. In a multilevel architecture, different pre-trained CNNs are adjusted to extract deep features and train two classifiers. The best model extracted visual features from the anterior camera angle in the experiments, and achieved a sensitivity value of 1.000 as the best result.