Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Abstract

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

“…The recent development of deep learning methods has drawn much attention for brain image analysis [ 13 , 14 , 15 ]. These methods may provide solutions for predicting molecular subtype gliomas by automatic feature learning.…”

A bird’s-eye view of deep learning in bioimage analysis

“…The impact of deep learning has been reviewed more specifically in a wide range of medical imaging areas, including abdominal imaging [103] , atherosclerosis imaging [104] , structural and functional brain imaging [105] , [106] , in-vivo cancer imaging [107] , dermatological imaging [108] , endoscopy [109] , mammography [110] , musculoskeletal imaging [111] , nuclear imaging [112] , ophthalmology [113] , pulmonary imaging [114] , thoracic imaging [115] , as well as in radiotherapy [116] , interventional radiology [117] , and radiology in general [118] , [119] , [120] . The massive body of papers on deep learning in virtually all areas of medical imaging has inspired many to write primers [121] , [122] , [123] , guides [124] , [125] , [126] , white papers or roadmaps [127] , [128] , [129] , and other commentaries [130] , [131] , [132] .…”

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

“…Much more work has done in the field of deep and machine learning for the past decade, and in the future, there is a chance that deep learning, machine learning, big data, and information retrieval will remain the most tempting areas for researchers in the medical and engineering fields [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”