Performance Analysis of Feature-Based Lung Tumor Detection and Classification

Background: Lung cancer is the leading cause of cancer death and it is identified at the ending stage of the severity. The differentiation between lesions and its background tissues are difficult task due to its low contrast between lesions and its background tissues. Lesion characterization is also a difficult task due to similar texture pattern between the lung tumors and normal lung tissues. Methods: In this paper, the computer aided automatic detection and classification of lung tumor is proposed. The multi resolution Gabor transform is applied over the lung image and then features such as local derivative and local ternary patterns are extracted from the transformed image. The extracted features are optimized by Genetic Algorithm (GA) and then classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. Conclusion: The proposed system for lung tumor detection system achieves 98.18% accuracy.