Lungs serve as the key organs of the human respiratory system, and any abnormalities can significantly disrupt respiratory functions. Lung cancer, known as the most lethal form of cancer, poses a serious threat to human health [1]. However, early detection greatly enhances the chances of successful treatment and improved prognosis. Deep learning techniques, have become instrumental in assisting medical professionals by automating Lung cancer detection and classification [2]. Although we have multiple medical imaging techniques—such as X-rays, Whole Slide Imaging (WSI), CT scans, and MRI—in our study, we have utilized lung CT images to comprehensively examine the efficiency of deep learning systems in the diagnosis and classification of pulmonary cancer. We have implemented VGG16 and VGG19 algorithms on IQ-OTH/NCCD Database for detection of lung cancer and obtained accuracy of 94.66 and 95.14 which are better than Simple CNN and Inception V3 earlier implemented on the same dataset in our previous research work [2].