Hybrid Deep Learning and Neuro-Fuzzy Approach for COVID-19 Diagnosis Using CT Scan Imaging: Integration of CNN, ANFIS, and PCA

Mohamed H Ghaleb Abdkhaleq
International Journal of Computational and Electronic Aspects in Engineering
Volume 6: Issue 2, June 2025, pp 81-88

Author's Information
Mohamed H Ghaleb Abdkhaleq 1 
Corresponding Author
1College of Computer Science and Information Technology, Wasit University, Wasit, Iraq
mghaleb@uowasit.edu.iq
Research Paper -- Peer Review
First online on – 1 June 2025

Open Access article under Creative Commons License

Cite this article –Mohamed H Ghaleb Abdkhaleq “,Hybrid Deep Learning and Neuro-Fuzzy Approach for COVID-19 Diagnosis Using CT Scan Imaging: Integration of CNN, ANFIS, and PCA”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 6, Issue 2, pp. 81-88, 2025.
https://doi.org/10.26706/ijceae.6.2.20250404

Abstract:-
The emergence of intelligent medical diagnostic systems fuelled by the pandemic is primarily focused on imaging data. While Convolutional Neural Networks (CNNs) are powerful tools in extracting visual features, their integration with neuro-fuzzy inference systems and dimensionality reduction methods like PCA enhance interpretability and performance on a broader scale. This work proposes a hybrid diagnostic framework for chest CT scan images that incorporates CNN for feature extraction together with PAS and ANFIS for/classification to accurately detect COVID-19. The system performs component extraction through CNN, reduces dimensionality by P mon, and uses ANIFIS to make the final class designation. The hybrid model is evaluated on a recent and thoroughly annotated dataset where it has proven to outperform the previous models in accuracy, robustness, ease of use and interpretative evaluation. This highlights the potential of the model for real-life application.
Index Terms:-
Convolutional Neural Networks (CNN), Chest CT Scan, Neuro-Fuzzy Inference System (ANFIS), Dimensionality Reduction (PCA), COVID-19 Diagnosis
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