Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier

ABSTRACT Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as...

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Bibliographic Details
Published in:International journal of imaging systems and technology 2018-06, Vol.28 (2), p.77-85
Main Authors: Herald Anantha Rufus, N., Selvathi, D.
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Brain
Brain cancer
brain tissues
brain tumors
classification
Classifiers
Evaluation
Fuzzy logic
Fuzzy systems
Image classification
Image segmentation
malignant
segmentation
Tumors
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Summary:ABSTRACT Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22258