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Abstract #82866 Published in IGR 20-4
Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Thakoor KA; Li X; Tsamis E; Tsamis E; Tsamis E; Sajda P; Hood DCConference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040
We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability maps. CNNs pretrained on natural images performed comparably to CNNs trained solely on OCT data, and all models showed high accuracy in detecting glaucoma, with receiver operating characteristic area under the curve (AUC) scores ranging from 0.930 to 0.989. Attention-based heat maps of CNN regions of interest suggest that these models could be improved by incorporation of blood vessel location information. Such CNN models have the potential to work in tandem with human experts to maintain overall eye health and expedite detection of blindness-causing eye disease.
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Classification:
1.6 Prevention and screening (Part of: 1 General aspects)
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.30 Other (Part of: 6 Clinical examination methods)
