Steerable wavelet transforms with modified extreme learning machine for lung segmentation and cancer detection in lung CT images - Vijayaraj, J.; D. Loganathan, Dr. (International Journal of Engineering and Technology, 2019)
Lung Segmentation is an imperative measure of both Computer Aided Detection (CAD) and Diagnosis frameworks for lung cancer, since the execution of those frameworks are relied upon the execution of the Lung Segmentation in Computed Tomography (CT) pictures of lungs. In this paper, a Modified Extreme Learning Machine (MELM) based classification algorithm is proposed to segment the tumor region of lung pictures. The fundamental guide of this work is to exhibit an algorithm for distinguishing the tumor from segmented lung pictures. In this way, this segmentation procedure, at first, the input CT lung pictures are pre- processed to evacuate Poisson noise by utilizing improved kaun filter. At that point, the lope of lung has been segmented by utilizing Steerable Wavelet Transform (SWT) for efficacious diagnosis. After lung segmentation, the Gray Level Co-Occurrence Matrices (GLCM) based features are utilized for tumor identification. In this process, the features are changed as an input to Modified Extreme Learning Machine (MELM), and it classifies the benevolent and harmful area of lung pictures. At long last, the threatening caner locales are segmented for diagnosis process. This exhibited scheme is assessed by utilizing open database. The simulation results comes about demonstrate the execution of presented scheme (i.e. MELM) has achieved better outcomes as far as high accuracy, purity, rand index, jaccard coefficient, F-measure, G-mean, precision and recall thought about than existing modal based classification algorithms.