Detection of tumor slice in brain magnetic resonance images by feature optimized transfer learning
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Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Aksaray Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study includes investigating the presence of tumor regions in Magnetic Resonance Imaging (MRI) slices. Since the MRI taken from a patient consists of many slices, it may take time for experts to review these images. The aim of the study is to evaluate the specialist's MRI slices more quickly. The image of each MRI slice taken from the patient was applied to the Alexnet transfer learning algorithm and the properties of the image were obtained. These features are optimized with the Relieff feature selection algorithm to achieve optimum success. The highest accuracy has been achieved with the support vector machine classifier, in which optimized features are used. The study was validated with 3 different combinations by training with two datasets and testing with the other. Thus, a method that can work under different conditions were obtained. The performance metrics of the study were obtained by taking the average of the successes obtained from each data set. MRIs were trained with Alexnet transfer learning model and performance analysis was performed on the obtained classification models. The feature optimization used both increased the success to 97.55% and reduced the processing time from 0.4064 to 0.3045 seconds. The proposed model with a high success rate and a rapid classification is expected to assist the expert in both diagnosis and treatment planning.
Açıklama
Anahtar Kelimeler
Brain Magnetic Resonance Imaging, Feature Extraction with Alexnet Transfer Learning, Relieff Feature Selection, Support Vector Machines
Kaynak
Aksaray University Journal of Science and Engineering
WoS Q Değeri
Scopus Q Değeri
Cilt
4
Sayı
2
