ClaRet -- A CNN Architecture for Optical Coherence Tomography
This work addresses retinal tear detection for medical imaging, but it appears incremental as it builds on existing methods like VGG-19 with custom layers.
The authors tackled the problem of detecting and classifying retinal tears from Optical Coherence Tomography scans by developing a CNN architecture called ClaRet, which achieved substantially better results than the baseline.
Optical Coherence Tomography is a technique used to scan the Retina of the eye and check for tears. In this paper, we develop a Convolutional Neural Network Architecture for OCT scan classification. The model is trained to detect Retinal tears from an OCT scan and classify the type of tear. We designed a block-based approach to accompany a pre-trained VGG-19 using Transfer Learning by writing customised layers in blocks for better feature extraction. The approach achieved substantially better results than the baseline we initially started out with.