Maryam Khoshkhooy Titkanlou - Master of Science, Biomedical engineering- Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran
Mostafa Kazemi - Master of Science, Electrical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Mohsen Amra - Master of Science, Financial Engineering, Department of Industrial Engineering, Islamic Azad university of south Tehran branch, Tehran, Iran

One of the main challenges in treating tumors and assessing disease progression is diagnosing tumor size And distinguish tumor types from each other. Manual tumor segmentation in three-dimensional Magnetic Resonance images (volume MRI) is a time-consuming and tedious task. Its accuracy depends heavily on the operator's experience doing it. The need for an accurate and fully automatic method for segmenting brain tumors and measuring tumor size is strongly felt. This paper first uses a combined CNN-LSTM method to detect HG and LG tumors in ۳D brain images. Then it used the UNET Neural Network to improve the location of the tumor in the brain. In this article, we use BRATS ۲۰۱۸ database images, And manual segmentation is used as the Grand truth. in this paper, we showed that the proposed method could effectively perform segmentation.

Brain Tumor, Deep learning, convolutional neural network