Artificial Intelligence-Driven Enhancement of X-Ray Images for Medical Detection

Abstract

Early disease prediction and diagnosis models have piqued the interest of medical professionals. A simple medical examination, such as a chest X-ray, is essential for diagnosing lung conditions such as lung cancer, COVID-19, pneumonia, tuberculosis, and pneumoconiosis. However, the advancements in medical imaging have practical implications for accurate diagnosis and patient treatment that extend beyond lab settings. Recently, image analysis capabilities have improved by integrating artificial intelligence (AI) with computer vision processing. The objective of this research is to improve X-ray images using AI techniques, thereby contributing to the developing medical scene. This research integrates image processing and machine learning techniques to enhance diagnostic accuracy in the medical imaging sector. It outlines the methods for analyzing and enhancing the stored X-ray images to identify potential diseases separating them from normal ones. A software Programme is developed for processing and classifying X-ray images using MATLAB. Median filtering and edge detection techniques of image processing are employed to significantly improve image clarity. These pre-processing steps ensure accurate classification of the images using a trained deep convolutional neural network (CNN) based model. This model is trained and validated on a labelled dataset of X-ray images, and implementation results exhibit high accuracy in detecting and classifying diseases. AI and image processing facilitate the prompt and accurate diagnosis, highlighting the critical role that AI plays in the medical industry.

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