Classification of retinal damage by a neural network based system.
The objective of this research is to provide an ophthalmologist with a helpful system, capable of classifying a degree of patients' retinal hemorrhage. The system is composed of four modules: (a) data acquisition module, (b) image Database module, (c) image processing module, (d) image classification module. The system was trained with a modular neural network on a set of 25 images, and tested on a set of 160 images. A training performance of greater than 95% was achieved. The classifying part of the system showed 79% recognition accuracy. Since the testing images were taken from independent sources, we assume that the system should also provide an accurate classification of other image types.
Aleynikov
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Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08855-0909, USA.
Micheli-Tzanakou
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D000465:Algorithms; D003628:Database Management Systems; D019992:Databases as Topic; D005103:Expert Systems; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D009885:Ophthalmology; D010363:Pattern Recognition, Automated; D012166:Retinal Hemorrhage / Q000145:classification*; D012984:Software