Pneumonia detection in chest X-ray images using Convolutional Neural Network and fuzzy VIKOR

Yousofvand, Leila; Dowlatshahi, Mohammad Bagher; Pirdadeh Beiranvand, Mostafa

doi:10.22111/ijfs.2025.51603.9119

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	Pneumonia detection in chest X-ray images using Convolutional Neural Network and fuzzy VIKOR
Iranian Journal of Fuzzy Systems
دوره 22، شماره 5، آذر و دی 2025، صفحه 159-179 اصل مقاله (1.07 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2025.51603.9119
نویسندگان
Leila Yousofvand¹؛ Mohammad Bagher Dowlatshahi^* ²؛ Mostafa Pirdadeh Beiranvand¹
¹Department of Computer Engineering, Lorestan University, Khorramabad, 68135-1911, Lorestan, Iran
²Associate Professor, Computer Engineering, Lorestan University
چکیده
Pneumonia is a life-threatening respiratory disease that requires early and accurate diagnosis for effective treatment and reduced complications. However, conventional diagnostic methods such as PCR are often time-consuming, equipment-dependent, and limited to specialized medical centers. This study introduces a novel anomaly detection framework for pneumonia diagnosis using advanced machine learning techniques applied to chest X-ray images. To enhance classification performance, the framework integrates several feature selection methods, including Correlation-based Feature Selection (CFS) to evaluate feature relevance, Fisher Score to rank features based on discriminative power, Maximum Information Coefficient (MIC) to capture complex dependencies, and Local Learning-based Correlation Feature Selection (LLCFS) to improve accuracy by considering local feature correlations. To further enhance classification performance, this study introduces the first-ever application of Fuzzy VIKOR in pneumonia detection. This fuzzy logic-based ensemble method effectively handles uncertainty in medical imaging data, leading to more balanced decision-making when dealing with conflicting information. The proposed model was trained on a chest X-ray dataset and evaluated using key classification metrics, including accuracy, recall, precision, and F1-score. Experimental results confirm that the model outperforms baseline methods across all metrics, achieving an accuracy of 98.34%. These findings validate the effectiveness of the proposed framework and highlight its high potential for real-world deployment in AI-driven computer-aided diagnosis (CAD) systems, particularly in hospitals and telemedicine applications.
کلیدواژه‌ها
Pneumonia Detection؛ Anomaly Detection؛ Fuzzy MCDM؛ Ensemble Feature Selection؛ Machine Learning

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Pneumonia detection in chest X-ray images using Convolutional Neural Network and fuzzy VIKOR