An Overview of Lung Cancer Classification Algorithms and their Performances

In the world, lung cancer is the third most dreadful cancer. Thus, detection of lung cancer cells at early stage is a challenge. The symptoms of lung cancer do not appear in earlier stages which causes high death rates when compared with other types of cancer. In lung cancer detection, image processing algorithms have shown great performance in various high-end tasks. In this paper, different classification methodologies used for the prediction of lung cancer in its early stage are explained. Machine learning techniques are used to identify whether lung tumors are malignant or benign. Machine learning approaches such as: Convolutional neural network (CNN), Support vector machine (SVM), Artificial neural network (ANN), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Entropy degradation method (EDM) and Random Forest (RF) are discussed in detail and their performance is evaluated in terms of accuracy, sensitivity and specificity. In this analysis, CNN approach using small dataset shows best result with 96% accuracy compared to other methodologies and EDM shows the worst accuracy of 77.8% © 2021. IAENG International Journal of Computer Science.All Rights Reserved