Hybrid classifier for brain abnormality detection in brain MRI

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Publicado en 3C Tecnología. Edición Especial/Special Issue – Noviembre/November 2021



Brain abnormality detection is very essential now a days. Brain abnormality includes both massive abnormal growth of cells called tumour and blood lump in a veins or artery. If The abnormalities are detected at the earlier stages it would help to save life. The symptoms for the abnormalities are same and vary with patient aging and severity of the abnormality. The algorithm which works for tumor can not be used for the detection of abnormality caused in blood lumps of veins and artery. Normally the tumor is the massive growth of the cell and the features can be extracted and applied for the classifier to identify the severity of the abnormality. But the detection of abnormality caused in vein or artery due to blood lumps are very difficult to identify and feature extraction is also difficult. A sophisticated algorithm should be used for identifying the blood lumps. This paper deals with hybrid classifier (SVM and ANFIS) for detecting the abnormalities such as tumour as well as stroke. Till now separate algorithms are used for detecting tumour or stroke from brain MR image. In our proposed work it is possible to identify stroke or tumour with same algorithm by using different hybrid classifiers. The proposed system helps the physicians to diagnose human brain stroke. Accuracy of 0.999, sensitivity 0.38, specificity 0.86, PPV 0.91, NPV 0.99 is obtained by ANFIS classifier. Three quantitative events to calculate brain tumor of average segmentation results: Similarity Index (SI), Overlap fraction (OF), and Extra Fraction (EF) is 0.776194, 0.775198, 0.222213 is obtained by SVM classifier.


Palabras clave

SVM, ANFIS, Brain Stroke, Tumour.

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