Lung cancer is considered as most common cause of cancer death than other. This kind of cancer is growing in human body without previous symptoms. So, using systems to diagnosis the patients in early stages is very essential and conducting studies in this field to find a good accuracy is also required. This research aims to examine the possibility of using of Deep Learning techniques for the lung cancer classification based on VGG-19 using images. Layer 6 and layer 7 of VGG19 were used. Also, new datasets will be created from these two layers named as statistical operations, which includes: average, minimum, maximum and combination between the two layers. Then, the datasets will be classified using different ML classifiers, which includes: KNN, Random Forest, Naïve Bayes and Decision Tree. Three scenarios will be used based on the training dataset size when classifying data. In the results, KNN scored the best accuracy (98.40), precision (0.98), recall (0.98) and F-measure (0.98). The results were nearly similar in all layers and scenarios; this means that the extracted features can provide high accuracy if applied in classification researches. It can be proved that the lung cancer can be detected with best accuracy even if the size of dataset in the training set was small. Also, the second-best accuracy after KNN algorithm is Random Forest in all layers and scenarios.