Performance Analysis of Feature-Based Lung Tumor Detection and Classification

Title:Performance Analysis of Feature-Based Lung Tumor Detection and Classification

Volume: 13

Author(s): Manoj Senthil Kailasam*Meeradevi Thiagarajan

Affiliation:

• Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu 638 060,India

Keywords: Computer Aided Diagnosis (CAD), classification, lung tumor, medical image processing, segmentation.

Abstract: 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.

Cite this article as:

Kailasam Senthil Manoj*, Thiagarajan Meeradevi, Performance Analysis of Feature-Based Lung Tumor Detection and Classification, Current Medical Imaging 2017; 13 (3) . https://dx.doi.org/10.2174/1573405612666160725093958

DOI https://dx.doi.org/10.2174/1573405612666160725093958	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603