Lung cancer is the leading cause of mortality worldwide, affecting both men and women equally. . When a radiologist focuses on the patient's body, it increases the strain on the radiologist, and the likelihood of missing pathological information, such as abnormalities, is also increased. One of the primary objectives of this project is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer more rapidly and accurately. Based on a unique picture feature, the proposed strategy k into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. According to the researchers, the proposed technique method DSC of 97.3%, a sensitivity of 96.5%, and a specificity of 94.1% when tested on the LuNa-16 dataset. At long last, this research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create CAD systems. Other objectives include making it possible to conduct research into lung segmentation and automated pulmonary nodule segmentation while also improving the power and effectiveness of computer-assisted diagnosis of lung cancer, which relies on correct pulmonary nodule segmentation to be successful.