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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Anitha, J. Kezi Selva Vijila, C. Hemanth, D.J. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of ECE, Karunya University, Coimbatore, India (Anitha, J.; Kezi Selva Vijila, C.; Hemanth, D.J.) |
| Abstract | Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible. This drawback is mainly due to the high convergence time period. In this paper, a modified Counter Propagation Neural Network is proposed to tackle this problem which eliminates the iterative training methodology which accounts for the high convergence time. To prove the efficiency, this technique is employed on abnormal retinal image classification system. Real time images from four abnormal classes are used in this work. An extensive feature vector is framed from these images which forms the input for the CPN and the modified CPN. The experimental results of both the networks are analyzed in terms of classification accuracy and convergence time period. The results suggest the superior nature of the proposed technique in terms of convergence time period and classification accuracy. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 241093 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424450534 |
| DOI | 10.1109/NABIC.2009.5393591 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-12-09 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | CPN Modified CPN Retinal images Artificial neural networks Retina Pattern recognition Neural network Counting circuits Convergence Neural networks Convergence rate Computer networks Iterative methods Image classification Biomedical imaging |
| Content Type | Text |
| Resource Type | Article |
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