Intelligent System for Stability Assessment of Chest X-Ray Segmentation Using Generative Adversarial Network Model with Wavelet Transforms

Abstract

Accurate segmentation of chest X-rays is essential for effective medical image analysis, but challenges arise due to inherent stability issues caused by factors such as poor image quality, anatomical variations, and disease-related abnormalities. While Generative Adversarial Networks (GANs) offer automated segmentation, their stability remains a significant limitation. In this paper, we introduce a novel approach to address segmentation stability by integrating GANs with wavelet transforms. Our proposed model features a two-network architecture (generator and discriminator). The discriminator differentiates between the original mask and the mask generated after the generator is trained to produce a mask from a given image. The model was implemented and evaluated on two X-ray datasets, utilizing both original images and perturbed images, the latter generated by adding noise via the Gaussian noise method. A comparative analysis with traditional GANs reveals that our proposed model, which combines GANs with wavelet transforms, outperforms in terms of stability, accuracy, and efficiency. The results highlight the efficacy of our model in overcoming stability limitations in chest X-ray segmentation, potentially advancing subsequent tasks in medical image analysis. This approach provides a valuable tool for clinicians and researchers in the field of medical image analysis.