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Cancers 2019, 11(1), 111;

A Review on a Deep Learning Perspective in Brain Cancer Classification

Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur 440012, India
Department of Computer Science and Engineering, Marathwada Institute of Technology, Aurangabad 431010, India
Global Biomedical Technologies Inc., Roseville, CA 95661, USA
Indian Institute of Information Technology, Nagpur 440012, India
Brown University, Providence, RI 02912, USA
Department of Neurology, University Medical Centre Maribor, 2000Maribor, Slovenia
Department of Cardiology, St. Helena Hospital, St. Helena, CA 94574, USA
Department of Neurosurgery, Greater Accra Regional Hospital, Ridge, Accra233, Ghana
Department of Radiology, Greater Accra Regional Hospital, Ridge, Accra233, Ghana
Department of Cardiology, Apollo Hospitals, New Delhi 110076, India
Neuro and Epileptology, BGS Global Hospitals, Bangaluru 560060, India
Department of Radiology, A.O.U., Cagliari 09128, Italy
Stoke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
Author to whom correspondence should be addressed.
Received: 29 November 2018 / Revised: 7 January 2019 / Accepted: 10 January 2019 / Published: 18 January 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
Full-Text   |   PDF [5628 KB, uploaded 18 January 2019]   |  
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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. View Full-Text
Keywords: cancer; brain; pathophysiology; imaging; machine learning; extreme learning; deep learning; neurological disorders cancer; brain; pathophysiology; imaging; machine learning; extreme learning; deep learning; neurological disorders

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Tandel, G.S.; Biswas, M.; Kakde, O.G.; Tiwari, A.; Suri, H.S.; Turk, M.; Laird, J.R.; Asare, C.K.; Ankrah, A.A.; Khanna, N.N.; Madhusudhan, B.K.; Saba, L.; Suri, J.S. A Review on a Deep Learning Perspective in Brain Cancer Classification. Cancers 2019, 11, 111.

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