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BRAIN TUMOR SEGMENTATION BASED ON SFCM USING PROBABILISTICNEURAL NETWORK WITH RASPBERRY PI


Abstract

Abstract: Calculatingautomatic defects detection in MR images is essential in many diagnostic andtherapeutic applications. Because of high quantity data in MR images andblurred boundaries, tumor segmentation and classification is very hard. Thisprocess has detection method to use increase accuracy and decrease diagnosistime. Particular process goal has to classify the tissues into three classes ofregular, begin and malignant. 

InMRI images, the amount of data is too much for manual interpretation andanalysis. Using brain tumor segmentation used magnetic resonance imaging (MRI),and his used become research area in medical image system . Brain tumordetection method is identified accurately of size and location of brain cancer(Tumor ) plays a vital role in the diagnosis of disease. The diagnosis methodconsists of four stages, pre-processing of MR images, feature extraction, andclassification.

AfterMRI Image classification equalization of the image, the features are extractedbased using Dual-Tree Complex wavelet transformation (DTCWT). In the laststage, Probability Neural Network (PNN) is employed to classify the Normal andabnormal brain. A practical algorithm is proposed for tumor detection based onthe Spatial Fuzzy C-Means Clustering. Raspberry pi kit using sending data todoctor mail id.

KeywordsMatlab S/W, Raspberry –pi Kit, Online Database images.

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