A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms

Tao Chen; Lianting Hu; Quan Lu; Feng Xiao; Haibo Xu; Hongjun Li; Long Lu

doi:10.3389/fnins.2023.1120781

A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms

Front Neurosci. 2023 Jul 7:17:1120781. doi: 10.3389/fnins.2023.1120781. eCollection 2023.

Authors

Tao Chen¹, Lianting Hu^{2

3}, Quan Lu⁴, Feng Xiao⁵, Haibo Xu⁵, Hongjun Li⁶, Long Lu^{4

7

8

9}

Affiliations

¹ School of Information Technology, Shangqiu Normal University, Shangqiu, China.
² Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
³ Guangdong Cardiovascular Institute, Guangzhou, China.
⁴ School of Information Management, Wuhan University, Wuhan, China.
⁵ Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
⁶ Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China.
⁷ Big Data Institute, Wuhan University, Wuhan, China.
⁸ School of Public Health, Wuhan University, Wuhan, China.
⁹ Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.

Abstract

The choice of treatment and prognosis evaluation depend on the accurate early diagnosis of brain tumors. Many brain tumors go undiagnosed or are overlooked by clinicians as a result of the challenges associated with manually evaluating magnetic resonance imaging (MRI) images in clinical practice. In this study, we built a computer-aided diagnosis (CAD) system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms. Neuroimages are specifically represented using a type of visual feature known as the histogram of gradients (HOG). Then, through a two-level classification framework, the HOG features are employed to distinguish between healthy controls and patients, or between different glioma grades. This CAD system also offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring. Finally, a knowledge base is created to offer additional advice for the diagnosis of brain tumors. Based on our proposed two-level classification framework, we train models for glioma detection and grading, achieving area under curve (AUC) of 0.921 and 0.806, respectively. Different from other systems, we integrate these diagnostic tools with a web-based interface, which provides the flexibility for system deployment.

Keywords: classification; computer-aided diagnosis (CAD) system; detection; glioma; grading; knowledge base; magnetic resonance imaging; segmentation.