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Comparative Study
. 2021 Aug 27;11(1):17318.
doi: 10.1038/s41598-021-96601-3.

ANFIS-Net for automatic detection of COVID-19

Afnan Al-Ali  1 , Omar Elharrouss  2 , Uvais Qidwai  2 , Somaya Al-Maaddeed  2
Affiliations
Comparative Study

ANFIS-Net for automatic detection of COVID-19

Afnan Al-Ali et al. Sci Rep. .

Abstract

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The workflow of the proposed method.
Figure 2
Figure 2
The structure of 2 inputs parameters of ANFIS.
Figure 3
Figure 3
Sample of the dataset. The first row shows sample of COVID-19 cases and the second row shows the normal cases.
Figure 4
Figure 4
The impact of range of influence on accuracy in tests 1, 2 and 3.
Figure 5
Figure 5
The impact of accept factor and reject factor on accuracy in test 4 and 5.
Figure 6
Figure 6
The impact of squash factor on accuracy in tests 6, 7, 8 and 9.
Figure 7
Figure 7
The best parameters values from previous test in tests 10, 11 and 12.
Figure 8
Figure 8
The relation between the generated rules and the processing time.
Figure 9
Figure 9
The ROC curve of the first six tests in our proposed method.
Figure 10
Figure 10
The ROC curve of the second six tests in our proposed method.
See this image and copyright information in PMC

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