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Automated Classification of Brain Images Using DWT and Biogeography-Based Optimisation
A. Agrawal, , Varun Kumar Kouda, Saladi Saritha
Published in IEEE
Pages: 204 - 209
Diagnose the abnormalities is the hardest step in the whole medical process. Early detection of abnormalities can save lot of time, efforts and resources. The wavelet transform is one of the better methods, to achieve excellent results in terms of accuracy. So, we proposed this new method for automated classification of MRI (Magnetic resonance imaging) brain images as normal or abnormal. PPCA is used for the feature reduction process as to get decrease the computation time and complexity. SVM (support vector machine) is a classifier and to optimise the weights of the SVM, BBO (biogeography based optimization) was used. The results we obtained in terms of accuracy is superior to the previously proposed methods. Three factors accuracy.precision and sensitivity from the confusion matrix are considered into account for the evaluation of the proposed method. © 2018 IEEE.
About the journal
JournalData powered by Typeset2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)
PublisherData powered by TypesetIEEE
Open AccessNo