Enhancing Pneumonia Diagnosis: A Fuzzy Expert System Leveraging Deep Learning Technologies
DOI:
https://doi.org/10.48047/7ayamp11Keywords:
.Abstract
Pneumonia, a major respiratory disease, presents a significant global health challenge that requires precise diagnostic methods. Our system addresses this need by employing an expert fuzzy logic approach to integrate key clinical parameters such as body temperature, sputum characteristics and color, chest pain, shortness of breath, respiratory rate, heart rate, systolic blood pressure, and white blood cell count. These factors are synthesized into a robust decision-making model, effectively capturing the complexity of pneumonia diagnosis. To enhance diagnostic accuracy, our approach incorporates chest X-ray images processed through Convolutional Neural Networks (CNNs), using models like ResNet-50.
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References
World Health Organization. (2021). Global Report on Respiratory Diseases. WHO Press.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
MedPage Today. (2019). New Guidelines for CAP in Adults: A Shift Toward Amoxicillin.
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