Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning

Ömer Kasım; Salih Çelik

doi:10.29002/asujse.820599

EN

Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning

Abstract

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.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Ömer Kasım ^*
0000-0003-4021-5412
Türkiye

Salih Çelik
Türkiye

Yayımlanma Tarihi

30 Aralık 2020

Gönderilme Tarihi

3 Kasım 2020

Kabul Tarihi

29 Aralık 2020

Yayımlandığı Sayı

Yıl 2020 Cilt: 4 Sayı: 2

DOI

https://doi.org/10.29002/asujse.820599

IZ

https://izlik.org/JA94TK39PD

Kaynak Göster

RIS / Bibtex

APA

Kasım, Ö., & Çelik, S. (2020). Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning. Aksaray University Journal of Science and Engineering, 4(2), 187-198. https://doi.org/10.29002/asujse.820599

Cited By

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Aksaray University Journal of Science and Engineering

https://doi.org/10.29002/asujse.1280766

Detection of Flatfoot Deformity from X-Ray Images Using Image Filtering and Transfer Learning Approaches

DÜMF Mühendislik Dergisi

https://doi.org/10.24012/dumf.1611410

Öz

Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Detection of Flatfoot Deformity from X-Ray Images Using Image Filtering and Transfer Learning Approaches