Lung cancer which is responsible for the most number of cancer related deaths, is a significant global health concern. Detecting lung cancer at later stages significantly increases the risk of death. However, there is a powerful tool in medical image classification that can identify diseases from medical images. Among various medical imaging techniques, Computed Tomography (CT) scans are particularly effective at providing detailed information about cells, tissues, and lung nodules, surpassing the capabilities of X-rays and Magnetic Resonance Images (MRI). In CT scans, lung nodules play an important role in detecting cancer. This paper focuses on the potential of deep learning, a tool that instills hope in identifying complex and irregular lung nodules in CT (Computed Tomography) scans, offering a promising future for cancer diagnosis.