Implementation of Image Processing Techniques for Viral Pneumonia Diagnosis Using Chest X-Ray Images - Wardani, Yulita Ayu (SAFE-Network, 2025)
The COVID-19 pandemic has increased the need for rapid and accurate diagnostic methods for viral pneumonia diseases. This study employs an experimental approach to analyze the application of image processing techniques for viral pneumonia diagnosis using chest X-ray images. The dataset consists of 100 chest X-ray images obtained from a public Kaggle repository, including normal and viral pneumonia cases. The proposed methodology involves several main stages. Image preprocessing is performed through image enlargement using bilinear interpolation and noise reduction using a median filter to improve image quality. Morphological operations, including erosion and dilation, are applied to enhance lung structures and clarify anatomical contours. Image enhancement is conducted using histogram equalization to improve contrast between healthy and infected regions. Finally, convolution-based edge detection using the Sobel operator is applied to highlight structural boundaries relevant to diagnostic interpretation. This processing framework aims to enhance image clarity and feature visibility, thereby supporting more efficient and consistent analysis of chest X-ray images for viral pneumonia diagnosis.