An intelligent algorithm for lung cancer diagnosis using extracted features from Computerized Tomography images

According to the World Health Organization, lung cancer is a leading cause of death worldwide. This research aims to process the Computerized Tomography (CT) images of lung cancer patients for the early diagnosis of the disease. The images are processed using Convolutional Neural Network (CNN) in the first approach, where Artificial Neural Network (ANN) is employed to classify the images. In the second approach, the images are pre-processed and segmented before utilizing CNN and ANN. In the third method, all the pre-processed and segmented images are converted to numerical data via specific feature extraction algorithms in the last step. Besides, dimensional reduction and feature selection algorithms are employed to classify with three machine learning techniques, i.e., Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM). An extensive comparative analysis is made to come up with the best technique. The comparisons are made by evaluating the methodologies on a set of lung CT scan images collected from a medical center. The results show that when either SVM or RF classification techniques are used, a 95% accuracy is obtained in diagnosing lung cancer.