Deep Learning Based Fully Automated Decision Making for Intravitreal Anti-VEGF Therapy
Intraocular anti-vascular endothelial growth factor (VEGF) therapy is the most significant treatment for vascular and exudative diseases of the retina. The highly detailed views of the retina provided by optical coherence tomography (OCT) scans play a significant role in the proper administration of anti-VEGF therapy and treatment monitoring. With increasing cases of visual impairment worldwide, computer-aided diagnosis of retinal pathologies is the need of the hour. Recent research on OCT-based automatic retinal disease detection has focused on using the state-of-the-art deep convolutional neural network (CNN) architectures due to their impressive performance in image classification tasks. However, these architectures are large in size and take significant time during testing, thus limiting their deployment to machines with ample memory and computation power. This paper proposes a novel deep learning based OCT image classifier, utilizing a small CNN architecture named as SimpleNet. It provides better classification accuracy with 800x fewer parameters, 350x less memory requirement, and is 50x faster during testing compared to state-of-the-art deep CNNs. Unlike other papers focusing on the prediction of specific diseases, we focus on broadly classifying OCT images into needing anti-VEGF therapy, needing simple routine care or normal healthy retinas.
KeywordsDeep learning Optical coherence tomography Anti-VEGF therapy
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