Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. (15th November 2019)

• Record Type: Journal Article
• Authors: Kasinathan, Gopi; Jayakumar, Selvakumar; Gandomi, Amir H.; Ramachandran, Manikandan; Fong, Simon James; Patan, Rizwan
• Is Part Of: Expert systems with applications. Volume 134(2019)
• Page Start: 112
• Page End: 119
• Publication Date: 2019-11-15
• Subjects: Image segmentation -- LIDC-IDRI data set -- Active contour model -- Inhomogeneity -- Multi-scale Gaussian distribution -- Enhanced CNN classifier; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.05.041
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A model is developed to help lung tumor prognosticate and find centroid displacement. The proposed model segment tumor portion and also extract features. An enhanced CNN based on AlexNet architecture is proposed. Enhanced CNN helps to classify CT images such as benign or malignant tumors. Abstract: The World Health Organization (WHO) recently reported that the lung tumor was the leading cause of death worldwide. In this study, a practical computer-aided diagnosis (CAD) system is developed to increase a patient's chance of survival. Segmentation is acritical analysis tool for dividing a lung image into several sub-regions. This work characterized an automated 3-D lung segmentation tool modeled by an active contour model for computed tomography (CT) images. The proposed segmentation model is used to integrate the local image bias field formulation with the active contour model (ACM). Here, a local energy term is specified by using the mean squared error to reconcile severely in homogeneous CT images and used to detect and segment tumor regions efficiently with intensity inhomogeneity. In addition, a Multiscale Gaussian distribution was applied to the CT images for smoothening the evolution process, and features were determined. For proposed model evaluation, were used the Lung Image Database Consortium (LIDC-IDRI) data set that consisted of 850 lung nodule-lesion images that were segmented and refined to generate accurate 3D lesions of lung tumor CT images. Tumor portions were extracted with 97% accuracy. Using continuous feature extraction of 3-D images leads to attributing the deformation and quantifies the centroid displacement. In this work, predict the centroid displacement and contour points by a curve evolution method which results in more accurate predictions of contour changes and than the extracted images were classified using an Enhanced Convolutional Neural Network (CNN) Classifier. The experimental result shows that the modified Computer Aided Diagnosis (CAD) system has a high ability to acquire good accuracy and assures automated diagnosis of a lung tumor.