Identifying pneumonia in lung CT scans is complex as the hidden symptoms of the disease are not evident and barely distinguishable by the naked eye, leading to probable misdiagnosis or loss of cases. Further, it is rather challenging to distinguish between the diseases because of the complex structure of the lungs and the variety of their pathology. This study’s originality is found in an image preprocessing approach that enhances the diagnosis of pneumonia from lung radiographs. Specifically, a wavelet-based image enhancement technique is used to improve the visibility of infection details in the images, the preprocessed images are fed into several deep learning networks, which classify the outputs into three categories: Pneumonia due to COVID-19, pneumonia due to other etiologies, and no findings. Furthermore, to measure the effectiveness of the suggested technique and the outcomes of this proposed preprocessing method, it experiments on various deep learning models such as DenseNet, ResNet50, and GoogLeNet. As a result, DenseNet achieved a better accuracy of 99.20%, which is higher than ResNet50 97.93%, and GoogLeNet 97.59%. The observations indicate that the proposed preprocessing leads to greater sensitivity and better results in diagnosing pneumonia, further stressing the applicability of this method to aid in early and accurate diagnosis potentially.