Tumor detection in MR images of regional convolutional neural networks


Ari A., HANBAY D.

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.34, ss.1396-1409, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 34 Konu: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.17341/gazimmfd.460535
  • Dergi Adı: JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
  • Sayfa Sayıları: ss.1396-1409

Özet

Human deaths caused by brain tumors are increasing nowadays. The brain tumor can grow very fast and can get twice of it is usual size. Therefore, physician have to analysis the Magnetic Resonance (MR) images quickly. This step is vital for the diagnosis of cancer, for treatment planning and for evaluation of the treatment outcome. If the patient who has the tumor in his brain is not treated correctly and quickly, the patient's chance of survival may decrease and resulted in death. In this article, we were developed a computer-assisted automated tumor detection system that can assist the physician in detecting and locating the tumor easily from brain MR images. The developed system is based on Regional based Convolutional Neural Networks (RCNN), which is one of the deep learning architectures. Besides RCNN is a structure that uses the architecture of the Convolutional Neural Networks (CNN), it can be considered as a structure in which the interested region is given as input in addition to the input images. In the proposed method, different RCNN architectures were designed and tested on Benchmark, Rembredant and Harvard datasets. The highest accuracy was obtained from the RCNN4 architecture on Benchmark data set is 99.10%. The highest average accuracy was calculated as 98.66% with RCNN4 architecture. Also, the success of the proposed method was compared with some of the methods exist in the literature. These comparisons showed that the proposed method is more successful and effective.