Chest X-Rays (CXRs) are the most performed radiological procedure, accounting for roughly one-third of all radiological procedures. These images are used to study various structures such as the heart and lungs to diagnose diseases like lung cancer, tuberculosis, and pneumonia. Anatomical structure segmentation in chest X-rays is a critical component of computer-aided diagnostic systems. The measurements of irregular shape and size and total lung area can provide insight into early signs of life-threatening conditions such as cardiomegaly and emphysema. Lung segmentation is a challenge due to variance caused by age, gender, or health status; it becomes even more difficult when external objects like cardiac pacemakers, surgical clips, or sternal wire are present. As a result, accurate lung field segmentation is regarded as an important task in medical image analysis. A comparison of the efficacy of two deep-learning algorithms to detect lung-related pathologies via an investigation into the size of the lungs is enumerated herein. Utilizing X-ray images and the accompanying masks, Deep Learning Models were employed to predict the lung masks respective to the X-Ray Images with an exceptional level of accuracy achieved by one of the Deep Learning models at a 99.64%, determining the lung condition if it is normal or abnormal by calculating the sizes of the lung mask.