Lung cancer is a disease in which the growth of cells in the lung goes out of control. This disease can be lethal if the treatment to stop the growth of cells is not given to the patient in its early stages. Hence, it is very crucial to correctly recognize lung cancer in less time. Using the traditional method where each tissue is observed by a medical practitioner is time-consuming as well as error-prone; moreover, the practitioner should be very skilled. All these problems can be solved by using automated methods to detect lung cancer. In this chapter different deep learning models and techniques are used to detect lung cancer using histopathological images. The accuracy achieved by these models is very high and takes negligible time to give the results. Using a pretrained ResNet model combined with a support vector machine accuracy of 98.57% is achieved on the test data.