Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

Abstract

One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.

Keywords

• feature selection
• relieff algorithm
• mobilenetv2
• brain tumor
• magnetic resonance imaging
• öznitelik seçimi
• relieff algoritması
• beyin tümörü
• manyetik rezonans görüntüleri
• öznitelik seçimi
• relieff algoritması
• beyin tümörü
• manyetik rezonans görüntüleri