Neural Network Analysis for the detection of glaucomatous damage |
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| Affiliation: | 1. School of Computer Science, University of Lincoln, Lincoln, United Kingdom;2. Department of Radiology and Biomedical Imaging, Musculoskeletal Quantitative Imaging Research Group, University of California, San Francisco, San Francisco, CA, United States;3. Optometry and Visual Science, School of Health Sciences, University of London, London, United Kingdom;4. Sunderland Eye Infirmary, South Shields and Sunderland City Hospitals NHS Foundation Trust, Sunderland, United Kingdom;1. Department of Mechanical Engineering, Amrita School of Engineering, Amritapuri, Amrita Vishwa Vidyapeetham, Amrita University, India;2. Intelligent Systems Research Centre, University of Ulster, Magee Campus, Northern Ireland, UK;3. College of Science and Technology, Nottingham Trent University, UK;4. Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria |
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| Abstract: | Glaucoma is a major cause of blindness and is prevalent among Asian populations. Therefore, early detection is of paramount importance in order to let patients have early treatments. One prominent indicator of glaucomatous damage is the Retinal Nerve Fiber Layer (RNFL) profile. In this paper, the performance of artificial neural network models in identifying RNFL profile of glaucoma suspect and glaucoma subjects is studied. RNFL thickness was measured using optical coherence tomography (Stratus OCT). Inputs to the neural network consisted of regional RNFL thickness measurements over 12 clock hours. Sensitivity and specificity for glaucoma detection will be compared by the area under the Receiver Operating Characteristic Curve (AROC). The results show that artificial neural network coupled with the OCT technology enhances the diagnostic accuracy of optical coherence tomography in differentiating glaucoma suspect and glaucoma from normal individuals. |
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| Keywords: | Neural network Glaucoma Optical coherence tomography |
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