Enhancing the Quality of chest X-ray images using GANs

Medical image processing plays an important role for developing strong diagnostic tools and improving healthcare outcomes. Low resolution chest X-rays often present great difficulty to machine learning models, that rely on high-quality inputs for accurate disease detection. This paper presents an Image Super-Resolution (ISR) technique using a Generative Adversarial Network (GAN) to enhance the resolution and clarity of chest X-ray images. Very often traditional approaches may fail in preserving critical details, the proposed model reconstructs high-resolution images with superior visual fidelity, which allows for better feature extraction for disease detection models. Experimental results demonstrate the effectiveness of the enhanced GAN model in improving image quality metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Thus, linking the combined challenges of medical imaging and machine learning offering a solution to enhance diagnosis and medical AI research.