Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.

ABSTRACT:

Background

Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI.

Methods

A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies.

Results

Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detecti

SUBMITTER: Kouli O 

PROVIDER: S-EPMC9234754 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature