A Short Note On Mr Image Classification Using Adaboost Essay
2265 Words10 Pages
MR Image Classification Using Adaboost
For Brain Tumor Types
Priyanka B. Zaware Electronics and Telecommunication Engineering
P.E.S Modern COE, Pune University
Pune, India firstname.lastname@example.org Prof. Rupali S. Kamathe Electronics and Telecommunication Engineering
P.E.S Modern COE, Pune University Pune, India email@example.com Abstract — Magnetic resonance imaging (MRI) is an crucial and most important technique used in the detection and classification of brain tumor. Brain MR imaging plays very a crucial role for radiologist to diagnose and treat brain tumour. Study of medical image by the radiologist is very time consuming and also the accuracy depends upon their experience and their expertise in that field. Thus computer aided systems become very necessary as they overcome the limitation.
This project presents an automated system of classification of tumor from brain MRI. The algorithm uses T2-weighted MRI images. The useful and important features of image are extracted from medical image for classification purpose. Here texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) method. The classification of MR images is done using Adaboost classifier. Then finally the performance of classifier is evaluated by sensitivity, specificity, error rate and accuracy.
Keywords— Brain MRI, computer aided systems, feature extraction, GLCM, Adaboost classifier.
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