A Review on a Deep Learning Perspective in Brain Cancer Classification

Abstract

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

“…• Artificial neural networks: ANNs are used in many works related to neuroimaging as classification method [15,[157][158][159][160][161][162][163] have been widely applied as a classifier to distinguish new test data. They are universal functional approximations allowing to approximate any function with arbitrary precision.…”

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

“…Besides these statistically derived CVD risk calculators, artificial intelligence (AI)-based techniques are also penetrating several medical imaging and risk assessment applications [46][47][48][49][50][51][52][53][54]. AI-based algorithms such as machine learning (ML) methods provide a better CVD risk assessment when compared with statistically derived conventional risk calculators [51,55,56].…”

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

“…In this work, we use DCNN for classification of brain cancer survival using whole slide histopathological images obtained from haematoxylin and eosin(H&E )-stained biopsy tissue sections, since no models were reported previously for classification of survival rates of brain cancer patients (see [38] for a comprehensive review on brain cancer classification using deep learning methods and MRI imaging). Moreover and although research is progressing on the molecular determinants that contribute to the development and growth of brain tumours, including glioblastoma, the most aggressive form, current classification approaches (either based on histological and/or genetic tests) do not directly focus on the survival of patients [1,2,10] and have not yet provided a complete picture on how "brain cancer type classification" can be used to predict (i) survival and (ii) response to treatment and (iii) help the development of more personalized treatments."…”