A Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI

Ufuq-i Dānish

article

Ayoub Najaf-Zadeh¹  Hamid Reza Ghaffari¹ 

[1] Department of Engineering Intelligence, Faculty of Engineering, Azad University, Ferdows Branch

关键词: Brain tumor;    MIR images;    Deep learning;    Two dimensional convolutional network;   

DOI  :  10.32598/hms.26.4.3303.1

学科分类：社会科学、人文和艺术（综合）

来源: Universidade Federal Rural De Pernambuco, Editora

【 摘 要 】

Aims: Cancerous brain tumors are among the most dangerous diseases that lower the quality of life of people for many years. Their detection in the early stages paves the way for the proper treatment. The present study aimed to present a two-dimensional Convolutional Neural Network (CNN) for detecting brain tumors under Magnetic Resonance Imaging (MRI) using the deep learning method. Methods & Materials: The proposed method has two stages of feature extraction and classification. A 12-layer CNN was used to extract the features of the MRI images and then the softmax activation function was used to classify these features. The proposed method was applied to a standard database consisting of three brain tumor types of meningioma, glioma, and pituitary. Findings: The proposed method had better performance compared to previously presented methods. Its accuracy was reported as 98.68%. Conclusion: Meningioma, glioma, and pituitary tumors are the most common types of brain tumors. Early detection of these tumors can decrease the risk of death. Because of its fully connected structure, the use of proposed deep CNN can help physicians to correctly detect brain tumors with MRI images.

【 授权许可】

CC BY-NC