Abstract:
The global prevalence of cancer is significant, and all are susceptible including
young and elderly, active and inactive, underweight and overweight.
According to the epidemiology, brain tumors account for 85% to 90% of all
primary central nervous system tumors. Brain tumors can be divided into
benign (non-cancerous) and malignant (cancerous). When using MRI to
diagnose tumors, it takes a lot of time and effort to manually classify MRI
images. Automatic or semiautomatic classification approaches are required in
order to distinguish between various tumor types because human observations
can result in classification errors. The purpose of this study is to perform a
statistical analysis to distinguish between benign and malignant brain tumors
and to develop a new method that can be used to reduce misclassification of
manual MRI observations. Using the MicroDicom viewer, T1-weighted and
T2-weighted axial images were selected. Tumor ROIs (region of interest) were
drawn using MATLAB. Some image features were calculated for each image
slice. Certain feature values were discovered for selected regions of benign
and malignant tumors. Features were compared to one another to determine
how they had changed. Machine learning (ML) algorithms for supervised
learning have a variety of formats. In this research work, LDA (Linear
Discriminant Analysis) was used to differentiate tumors using python. The
accuracy of LDA algorithm was 92.59%. This analysis can be used to
differentiate the tumors with high accuracy.
