This paper primarily deals with spatial domain filtering methods as a means of enhancing image quality. Images often suffer from various types of noise, such as Gaussian noise, speckle noise, and salt-and-pepper noise, which can obscure vital information within the image. To mitigate these noise artefacts, this study seeks spatial domain techniques, encompassing both linear and nonlinear spatial filtering methods. Furthermore, the paper explores the practical application of convolutional neural networks to enhance the quality of chest X-ray images using spatial filtering techniques for noise reduction. The experimentation involves two distinct chest X-ray images, serving as the basis for all computational assessments. Ultimately, the outcomes of this investigation are illustrated through graphical representations, detailing the processing time elapsed for both chest X-ray images.