Differentiation between Solitary Cerebral Metastasis and Astrocytoma on the Basis of Subventricular Zone Involvement on Magnetic Resonance Imaging

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

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.

Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.