Intelligent X-Ray Image Analysis using Fuzzy Inference Systems

Abstract

  The cumulative experience of a physician or x-ray technician enables them to distinguish
differences and changes in x-ray images, but they may not be completely accurate or automated for all
images. Fuzzy inference system can be recruited to automate identify and quantify important characteristics
of x- ray images (edges, textures, shapes, and intensity patterns. In this work, we proposed a novel technique
for contrast x-ray image enhancement and feature extraction based on fuzzy logoc system. AI-driven feature
extraction enhances the accuracy of medical diagnoses and reduce the need for manual preprocessing and
speeds up analysis, making it useful in high-volume clinical settings. In this study, a novel method is
proposed to enhance the content of X-ray images by improving contrast and facilitating the extraction of
critical features that aid medical professionals in image interpretation and analysis. The enhancement
technique was developed using fuzzy logic, a branch of artificial intelligence known for handling
uncertainty and imprecision in image data. Experimental results validate the effectiveness of the proposed
method, as evidenced by improvements in key quantitative metrics. In the future, it is possible to improve
the performance of this technique by combining it with deep learning technology and applying it to other
types of medical images.