Lung cancer is one of the most common lethal type of diseases. One of the most important
and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung cells from
the Computed Tomography (CT) images result. Segmentation and classification of lung CT image,
based on soft computing, is still a challenging task in the medical field, due to more computational
time and accuracy. This paper deals with an improvement in lung cancer detection using Possibilistic
Fuzzy C-Means (PFCM) based segmentation. This work also focuses on the normal and abnormal
cancer cells that is classified by using the algorithms of SVM (Support Vector Machine), Gaussian Interval
Type II Fuzzy Logic System and Genetic Algorithm (SVMFLGA). The results demonstrate that the SVMFLGA
outperforms the Gaussian Interval type II fuzzy logic system (GAIT2FLS) in terms of classification accuracy.
Keywords: Adaptive Network Fuzzy Inference System (ANFIS), Fuzzy Possibilistic C-Means Algorithm (FPCM), Gray Level
Co-Occurrence Matrix (GLCM), Non-Small Cell Lung Carcinoma (NSCLC), Small Cell Lung Carcinoma (SCLC), Type II
Fuzzy Logic System (T2FLS).
Rights & PermissionsPrintExport