Performance analysis of classifier for brain tumor detection and diagnosis. (July 2015)

• Record Type: Journal Article
• Authors: Shanthakumar, P.; Ganeshkumar, P.
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 302
• Page End: 311
• Publication Date: 2015-07
• Subjects: Brain tumor -- ANFIS -- Classifier -- Detection -- Segmentation -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.05.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: Diagnosis and proper treatment of brain tumors are essential to prevent permanent damage to the brain or even patient death. The brain magnetic resonance (MR) images are used to detect the severities of tumor detections. The performance analysis is measured on various criteria. This work is the differentiation of brain abnormalities from the healthy brain tissue. Abstract: Indefinite and uncontrollable growth of cells leads to tumors in the brain. The early diagnosis and proper treatment of brain tumors are essential to prevent permanent damage to the brain or even patient death. Accurate data regarding the position of the tumor and its size are essential for effective treatment. Hence, an entirely computerized automatic system to provide accurate tumor data is compulsory for physicians. Such developments are necessary to diagnose brain tumors during brain surgery. Brain magnetic resonance (MR) images are proposed for the detection and segmentation of the tumor region via a completely automatic and highly accurate method. The approach discussed in this paper employs an adaptive neuro fuzzy inference system (ANFIS) based on the automatic seed point selection range. The pixels intensity of the proposed algorithm is not dependent on the tumor type. The tumor's segmentation results are evaluated based on various criteria, including similarity index (SI), overlap fraction (OF), extra fraction (EF) and positive predictive value (PPV), which corresponded to values of 0.817%, 0.817%, 0.182%, and 0.817%, respectively, in this study. These results indicate that the approach proposed in this study performs better compared to many conventional processes. The significance of this work is the differentiation of brain abnormalities from the healthy brain tissue.