Cancer disorders are caused when certain body cells enlarge uncontrollably and spread throughout the body. Lung cancerous lesion tumors can be detected by radiologists with the help of medical imaging techniques like Computed Tomography (CT) scan images, by using chest X-rays, or by using MRI. We examine various medical image-based datasets, their availability, and the number of cases they contain. Due to the abundance of pictures, a CT scan-based strategy is employed. The vastness of the collection made it difficult for radiologists to diagnose quickly and correctly. Among the data that was supplied, there is a great deal of computer tomography (CT) scan pictures. Chest radiographs are the source of diagnostic mistakes in around ninety percent of instances involving lung cancer. In chest radiographs, it might be difficult for radiologists to differentiate a lung lesion from bones, pulmonary arteries, mediastinal structures, and other complicated anatomical features. This can be a challenge for diagnostic purposes. In order to tackle this issue, distinct predictive analytics and intelligent retrieval techniques are employed, like traditional Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), Dens-Net, Agile CNN, and LeNet. These deep structured learning-based methods mostly used complex neural networks, which caused vanishing gradient problems. To solve such a problem, we are using the ResNet50 method approach to get better result accuracy. CT scan images from (LIDC/IDRI) are being used for the dataset

AlexNet, GoogleNet, ResNet50, Convolutional neural network (CNN), lung cancer, transfer learning, Lung Imaging Database Consortium (LIDC)