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Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have been damaged. Early detection, on the other hand, aids in the control of the disease. It causes lung scarring as a result. The first methodology characterizes lung tissue utilizing first order statistics, grey live occurrence, run length matrices, and fractal analysis. It was suggested by Uppaluri et al in one instance. In the pre-processing step, patients' CT scans are presented using various color map models for better understanding of data-set. and also for determining the patients final Force Vital Capacity and Confidence values using a Pytorch model with leaky relu activation function. These variables can be used to determine whether a person has a disease. Segmentation is a crucial stage in employing a computer assisted diagnosis system to estimate interstitial lung disease. Accurate segmentation of aberrant lung is essential for a trustworthy computer-aided illness diagnosis. Using separate training, validation, and test sets, we proposed an efficient deep learning model using Unet architecture and Densenet121 to segment lungs with Interstitial Lung Disease. The proposed segmentation model distinguishes the exact lung region from the ct slice background. To train and evaluate the algo rithm, 176 sparsely annotated Computed Tomography scans were utilized. The training was completed in a supervised and end to end manner. Contrary to current approaches, the suggested method yields accurate segmentation results without the requirement for re-initialization. We were able to achieve an accuracy of 92.59 percent after training the proposed model with Nvidia's CUDA GPU.