<p>Lung abnormalities are a frequent medical imaging case, which must be detected effectively and promptly to be treated. This paper introduces a convolutional neural network (CNN) of ResNet-18, which is introduced to detect lung abnormalities in the X-rays of the chest. The suggested model is meant to detect various forms of lung abnormalities with well-selected datasets in collaboration with the best prepreprocessing and training approaches. Particularly, lung segmentation, outlier elimination, region-of-interest (ROI) extraction, and image normalization were combined to improve the quality of the data and minimize noise before classification. The system was able to attain an accuracy of 93.10% and an AUROC of 95.21 on a set of 1,740 chest radiographs, with such values approaching 100% by this designed preprocessing pipeline and effective model setup. These findings prove that performance was attained by optimizing methodologies and not just the scale of dataset. The results suggest that deep CNNs with convincing preprocessing and data refinement schemes have a high potential to effectively and reliably identify the pathological areas in the radiograph of the chest along with minimizing the amount of time and effort, which is needed to perform a manual analysis. The proposed system has a great potential to be integrated into clinical workflows in real-time to monitor clinical outcomes and aid in the development of the accuracy of diagnoses and promote early detection of diseases, due to its ability to provide fast and high-precision assessment. On the whole, this paper will emphasize the significance of the optimized data preparation and model design in the development of deep learning-powered applications in personalized and efficient healthcare delivery.</p>