Article: A fusion of fuzzy logic induced local binary pattern for COVID-19 detection from chest X-ray images: interpretation using layer-wise relevance propagation Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2025 Vol.48 No.3 pp.287 - 312 Abstract: According to current medical research, COVID-19 induced patient mortality may be reduced from early identification through precise diagnosis, especially in asymptotic cases. In the proposed work, a machine learning model is designed using fuzzy logic and local binary pattern of chest X-ray images to generate the fuzzy local binary pattern (FLBP) to extract the features of the X-ray images. The proposed convolutional neural network (CNN) model is trained employing these FLBP features in order to determine whether the subject having that X-ray image is healthy or COVID-19-affected. The model is trained with three datasets: Dutta_Saha_Saha, COVID-19 radiography database and novel COVID-19 chest x-ray repository achieving accuracy of 97.17%, 96.82% and 97.20% respectively. Additionally, using layer-wise relevance propagation, a relevance map of the FLBP image is created, emphasizing the key features of the image that are utilised to predict the class of the chest X-ray images using the proposed CNN classifier. Inderscience Publishers - linking academia, business and industry through research

Title: A fusion of fuzzy logic induced local binary pattern for COVID-19 detection from chest X-ray images: interpretation using layer-wise relevance propagation

Authors: Sushmita Pramanik Dutta; Sriparna Saha; Sakkya Singha Saha

Addresses: Department of Computer Science, Bijoy Krishna Girls' College, Howrah, India; Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal, India ' Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal, India ' New Medicate Diagnostic Center, B7/13(S), Central Park, Kalyani, West Bengal 741235, India

Abstract: According to current medical research, COVID-19 induced patient mortality may be reduced from early identification through precise diagnosis, especially in asymptotic cases. In the proposed work, a machine learning model is designed using fuzzy logic and local binary pattern of chest X-ray images to generate the fuzzy local binary pattern (FLBP) to extract the features of the X-ray images. The proposed convolutional neural network (CNN) model is trained employing these FLBP features in order to determine whether the subject having that X-ray image is healthy or COVID-19-affected. The model is trained with three datasets: Dutta_Saha_Saha, COVID-19 radiography database and novel COVID-19 chest x-ray repository achieving accuracy of 97.17%, 96.82% and 97.20% respectively. Additionally, using layer-wise relevance propagation, a relevance map of the FLBP image is created, emphasizing the key features of the image that are utilised to predict the class of the chest X-ray images using the proposed CNN classifier.

Keywords: COVID-19; fuzzy local binary pattern; FLBP; convolutional neural network; CNN; layer-wise relevance propagation; LRP.

DOI: 10.1504/IJBET.2025.148121

International Journal of Biomedical Engineering and Technology, 2025 Vol.48 No.3, pp.287 - 312

Received: 08 Sep 2024
Accepted: 27 Dec 2024
Published online: 25 Aug 2025 *

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Title: A fusion of fuzzy logic induced local binary pattern for COVID-19 detection from chest X-ray images: interpretation using layer-wise relevance propagation

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