A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network. (June 2020)

• Record Type: Journal Article
• Authors: Huang, Zheng; Xu, Han; Su, Shun; Wang, Tianyu; Luo, Yang; Zhao, Xingang; Liu, Yunhui; Song, Guoli; Zhao, Yiwen
• Is Part Of: Computers in biology and medicine. Volume 121(2020)
• Publication Date: 2020-06
• Subjects: Brain tumor diagnosis -- Differential feature neural network -- Magnetic resonance imaging; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103818
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: To improve the performance of brain tumor diagnosis, numerous automatic brain tumor diagnosis systems that use machine learning technologies have been proposed. However, most current systems ignore the structural symmetry of brain magnetic resonance imaging (MRI) images and regard brain tumor diagnosis as a simple pattern recognition task. As a result, the performance of the current systems is not ideal. To improve the performance of the brain tumor screening process, an innovative differential feature map (DFM) block is proposed to magnify tumor regions, and DFM blocks are further combined with squeeze-and-excitation (SE) blocks to form a differential feature neural network (DFNN). First, an automatic image rectification method is applied so that the symmetry axes of brain MRI images are approximately parallel to the perpendicular axis. Moreover, a DFNN is constructed to classify the brain MRI images into two categories: "abnormal" and "normal". The experimental results show that the average accuracy of the proposed system on two databases can reach 99.2% and 98%, and the introduction of the proposed DFM block can improve the average accuracy on these two databases by 1.8% and 1.3%, respectively, which indicates that the proposed DFM block can improve the performance of the brain tumor screening process. Highlights: More than 10, 000 brain MRI images are employed in the proposed system. A novel DFM block is proposed to detect the structural features of brain images. SE blocks are combined with DFM blocks to form a DFNN. The proposed system can achieve state-of-the-art performance.