Lung cancer classification using fuzzy logic for CT images. (2015)

• Record Type: Journal Article
• Authors: Kuruvilla, Jinsa; Gunavathi, K.
• Is Part Of: International journal of medical engineering and informatics. Volume 7:Number 3(2015)
• Page Start: 233
• Page End: 249
• Publication Date: 2015
• Subjects: computed tomography -- fuzzy inference systems -- FIS -- adaptive neuro fuzzy inference system -- ANFIS -- gray level co-occurrence matrix -- GLCM -- principal component analysis -- PCA -- lung cancer classification -- fuzzy logic -- neural networks -- CT images -- image segmentation -- cluster shade -- dissimilarity -- difference variance -- skewness -- medical images; 610.2805
• Languages: English
• ISSNs: 1755-0653

Abstract

In this paper a computer aided classification method for computed tomography (CT) images of lungs using fuzzy inference system (FIS) and adaptive neuro fuzzy inference system (ANFIS) is proposed. The entire lung lobe is segmented from the CT images using morphological operations. Statistical and gray level co-occurrence matrix (GLCM) parameters are calculated from the segmented image. Among 14 GLCM parameters and three statistical parameters, four parameters are selected for classification by principal component analysis. The parameters selected are cluster shade, dissimilarity, difference variance and skewness. The classification process is done by FIS and ANFIS. Compared to FIS, ANFIS gives better classification. A new training algorithm is proposed for the back propagation neural network used in the ANFIS. The proposed method gives a classification accuracy of 94% with a specificity of 100% and accuracy of 93%.