Abstract
Brain cancer has remained one of the key causes of
deaths in people of all ages. One way to survival amongst patients
is to correctly diagnose cancer in its early stages. Recently
machine learning has become a very important tool in medical
image classification. Our approach is to examine and compare
various machine learning classification algorithms that help in
brain tumor classification of Magnetic Resonance (MR) images.
We have compared Artificial Neural Network (ANN), K-nearest
Neighbor (KNN), Decision Tree (DT), Support Vector Machine
(SVM) and Naïve Bayes (NB) classifiers to determine the
accuracy of each classifier and find the best amongst them for
classification of cancerous and noncancerous brain MR images.
We have used 86 MR images and extracted a large number of
features for each image. Since the equal number of images, have
been used thus there is no suspicion of results being biased. For
our data set the most accurate results were provided by ANN. It
was found that ANN provides better results for medium to large
database of Brain MR Images.
Lubna Farhi, Razia Zia, Zain Anwar Ali. (2018) Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images, Sir Syed University Research Journal of Engineering & Technology, Volume-08, Issue-1.
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