TY  - JOUR
T1  - Classification of Brain Tumor MRI Image using Random Forest Algorithm and Multilayers
Perceptron
AU - Soesanti, Indah AU - Hadi, Meidar AU - , Avizenna AU - Ardiyanto, Igi 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 19
SP  - 3385
EP  - 3390
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.3385.3390
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.3385.3390
KW  - MRI
KW  -feature extraction
KW  -statistical texture
KW  -GLCM
KW  -classification
KW  -random forest
AB  - Magnetic Resonance Imaging (MRI) is a
medical technique commonly used by radiologists to
visualize organ structures in humans without surgery.
Based on histopathological appearance, the World Health
Organization (WHO) classifies premier tumors into Low
Grade Glioma (LGG) and High Grade Glioma (HGG).
The process of selecting a tumor area is usually done
manually by a radiologist, the process takes a lot of time
and effort. To help provide a second opinion for
radiologists in the classification of LGG and HGG brain
tumors, a computerized system is needed to process ROI,
feature extraction and MRI image classification. This
study aims to compare the classification results with the
ROI process and without the ROI process. 1000 images in
the form of 500 LGG Flair MRI images and 500 MRI
images of Flair HGG were processed by determining the
ROI of tumor images compared to without the ROI
processing being performed. The feature extraction
process uses statistical texture histogram equalization
method by calculating variance, skewness, kurtosis and
GLCM texture using Energy, Contrast, Entropy,
Homogeneity, Correlation, SumAverage, Variance,
Dissimilarity, Auto Correlation. Finally, the Random
Forest model is used to classify LGG and HGG class
images and be evaluated by k-fold validation validation
with k = 7. The results obtained from the proposed
method of accuracy, sensitivity and specificity reached
83.6% accuracy, 80.88% sensitivity and 86.84%
specificity. Shows that the method used to classify with
ROI results in an increase with an accuracy of 4%,
sensitivity increases by 4.46% and a specificity of 3.33%.
So that, the results obtained accuracy of 87.6% accuracy,
85.34% sensitivity and 90.17% specificity.
ER  -