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Updated: Sep 15, 2026
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
ANFIS-Net for automatic detection of COVID-19
Afnan Al-Ali1, Omar Elharrouss2, Uvais Qidwai2
1Department of Computer Science and Engineering, Qatar University, Doha, Qatar. aa1805360@qu.edu.qa.
Insights
This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) for automated COVID-19 detection from chest X-rays. The ANFIS model achieves high accuracy, comparable to deep learning, even with small datasets.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Infectious diseases, including COVID-19, are a leading global cause of mortality.
- The rapid spread of COVID-19 necessitates efficient and automated diagnostic tools.
- Fuzzy logic offers a robust method for handling uncertainty in medical diagnoses.
Purpose of the Study:
- To propose an Adaptive Neuro-Fuzzy Inference System (ANFIS) for automatic COVID-19 detection.
- To utilize texture analysis via Gray Level Co-occurrence Matrix (GLCM) for feature extraction from chest X-ray images.
- To demonstrate the efficacy of ANFIS, particularly on small datasets, in contrast to deep learning methods.
Main Methods:
- Chest X-ray images were analyzed using texture analysis with the Gray Level Co-occurrence Matrix (GLCM) technique.
- An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed for automated classification.
- The ANFIS model's performance was evaluated and compared against existing state-of-the-art techniques.
Main Results:
- The proposed ANFIS-based method demonstrated promising performance accuracy in detecting COVID-19 from chest X-rays.
- The ANFIS approach achieved performance comparable to complex deep learning architectures.
- A key advantage of the ANFIS method is its ability to function effectively with small datasets.
Conclusions:
- The ANFIS model presents an efficient and accurate solution for automated COVID-19 diagnosis using chest X-ray images.
- This approach offers a viable alternative to deep learning, especially when limited data is available.
- The ANFIS system shows potential for assisting healthcare professionals in rapid patient assessment.
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.
