This project attempts to evaluate the discriminative power of several predictors in the study to increase the efficiency of lung cancer detection through their symptoms. This project presents an approach which utilizes a LSTM to classify the tumours found in lung as malignant or benign. The accuracy obtained which is more efficient when compared to accuracy obtained by the traditional neural network systems. Lung cancer is one of the most lethal cancer types; thousands of peoples are infected with this type of cancer, and if they do not discover it in the early stages of the disease, then the chance of surviving of the patient will be very poor. For the suggested reasons above and to help in overcoming this terrible, early diagnosis with the assistance of artificial intelligence procedures most needed. Through this research, a Computer-aided system introduced for detecting lung cancer in a dataset collected from the Iraqi hospitals by using a convolutional neural network technique for helping with the diagnosis of the patient’s cases: normal, benign, or malignant.