This article offerings an overview of deep learning for lung infection location in images specially in medical domain. Disease is the main source of death for all kinds of people. The initial discovery of malignant development is valuable in dismissing the illness completely. The image processing events are generally utilized for forecast of lung cancer and furthermore for early identification and therapy to forestall the lung cancer. To anticipate the lung cancer in the lungs different features are separated from the images thusly, Neural network-based methodologies are valuable to foresee the lung cancer. The essential point of this work is to foster a high-level Computer Aided Diagnosis (CAD) framework utilizing deep learning technique that will effectively separate information from Computer Tomography (CT) scan images and give exact and ideal finding of cellular breakdown in the lungs. The study persuades the author to think that deep learning could be a useful asset in diagnosing tiny and exceptionally difficult to decide nodules to help clinical dynamic cycle.