Automatic RNA virus classification using the Entropy-ANFIS method
| Autor: | Engin Avci, Oznur Erkus, Esin Dogantekin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Logarithm
Feature vector Entropy Feature extraction Image processing FCM fuzzy c-mean Article Clustering ANFIS Adaptive Network Fuzzy Inference System DNA deoxyribonucleic acid Artificial Intelligence Electrical and Electronic Engineering Cluster analysis ANFIS Mathematics Adaptive neuro fuzzy inference system Quantitative Biology::Biomolecules Pixel biology FCM business.industry Applied Mathematics Pattern recognition RNA virus biology.organism_classification Classification RNA virus images Computational Theory and Mathematics Signal Processing RNA ribonucleic acid Computer Vision and Pattern Recognition Artificial intelligence Statistics Probability and Uncertainty Center-edge change method business |
| Zdroj: | Digital Signal Processing |
| ISSN: | 1095-4333 1051-2004 |
| Popis: | Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depends on manual controls. Moreover, detailed knowledge about RNA viruses is required. This study introduces the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS method), which can be used to automatically detect RNA virus images. This system consists of four stages: pre-processing, feature extraction, classification and testing the Entropy-ANFIS method with respect to the correct classification ratio. In the pre-processing stage, a center-edge changing method is used, in which the Euclidian distances are calculated from the center pixels to the edges of the imaged object. In this way, the distance vector is obtained. This calculation is repeated for each RNA virus image. In feature extraction, stage norm entropy, logarithmic energy and threshold entropy values are calculated to form the feature vector. The obtained feature vector is independent of the rotation and scale of the RNA virus image. In the classification stage, the feature vector is given as input to the ANFIS classifier, ANN classifier and FCM cluster. Finally, the test stage is performed to evaluate the correct classification ratio of the Entropy-ANFIS algorithm for the RNA virus images. The correct classification ratio has been determined as 95.12% using the proposed Entropy-ANFIS method. Highlights ► We present an Entropy-ANFIS method for detecting the RNA viruses by image processing. ► We used the center-edge changing method for pre-processing. ► We calculated the norm, the logarithmic energy and threshold entropy values. ► The performance of this new system is compared with ANN classifier and FCM cluster. |
| Databáze: | OpenAIRE |
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