Lung Cancer detection making use of medical imaging is still a challenging task for radiologist. The objective of this researchis to classify the types of lung tumours for extracted and selected features using learning algorithms. In this paper, an experimental study is conducted on 100 cases of lung cancer to evaluate the performance of learning classifiers (DNN, SVM,Random Forest, Decision Tree, Naïve Bayes) with different medical Imaging (DICOM) features to identify the two types ofLung cancer (Benign and Malignant). The proposed methodology intends to automate the entire procedure of diagnosis byautomatically detecting the tumor, measuring the required values such as diameter, perimeter, area, centroid, roundness,indentations and calcification. Experiment is conducted in to two phases: In the first phase, identify the most significantfeature used in lung cancer analysis by CT scan and perform the mapping to computer related format. In the second phase,feature selection and extraction is performed to machine learning algorithms. To evaluate the performance of classifiers interm of classification accuracy and improving the false positive rate, every stage of evolution is divided into four differentphases: single phase module, single slice testing, series testing and testing of learning algorithms. Experimental resultsshow significant improvement in false positive rate up to 30% for both Benign and Malignant. Whereas, Deep NeuralNetwork (DNN) demonstrate high values in term of classification accuracy in comparison with other classifiers. The proposed methodology for lung cancerdetection system having a potential to reduce the time and cost of diagnosis procedureand use for early detection of lung cancer.