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Automatic RNA virus classification using the Entropy-ANFIS method.

Esin Dogantekin1, Engin Avci2, Oznur Erkus3

  • 1Zirve University, Emine Bahaeddin Nakiboglu Medical Faculty, Department of Microbiology, 27260 Gaziantep, Turkey.

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|April 28, 2020
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Summary
This summary is machine-generated.

A new Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS) method automatically detects RNA virus images. This AI-driven approach achieves a 95.12% correct classification ratio, improving upon manual methods.

Keywords:
ANFISANFIS, Adaptive Network Fuzzy Inference SystemCenter-edge change methodClassificationClusteringDNA, deoxyribonucleic acidEntropyFCMFCM, fuzzy c-meanRNA virus imagesRNA, ribonucleic acid

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Area of Science:

  • Medical image processing
  • Virology
  • Artificial Intelligence

Background:

  • Traditional RNA virus identification via electron microscopy is manual, time-consuming, and requires expert knowledge.
  • Automated methods are needed to improve the efficiency and accuracy of virus detection.

Purpose of the Study:

  • To introduce and evaluate the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS) for automated RNA virus image detection.
  • To compare the performance of the proposed method against traditional techniques.

Main Methods:

  • The Entropy-ANFIS method involves four stages: pre-processing using a center-edge changing technique, feature extraction (calculating norm entropy, logarithmic energy, threshold entropy), classification using ANFIS, ANN, and FCM, and testing.
  • Feature extraction yields a rotation- and scale-independent feature vector.

Main Results:

  • The proposed Entropy-ANFIS method achieved a correct classification ratio of 95.12% for RNA virus images.
  • The system automates the detection process, reducing reliance on manual interpretation.

Conclusions:

  • The Entropy-ANFIS method offers a highly accurate and efficient automated solution for RNA virus image detection.
  • This AI-based approach has the potential to significantly advance medical image processing in virology.