Role of deep learning in brain tumor detection and classification (2015 to 2020): A review. (July 2021)

• Record Type: Journal Article
• Authors: Nazir, Maria; Shakil, Sadia; Khurshid, Khurram
• Is Part Of: Computerized medical imaging and graphics. Volume 91(2021)
• Publication Date: 2021-07
• Subjects: Brain tumor -- Deep learning -- Machine learning -- Neural networks; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101940
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: The paper deeply presents the review of studies on brain tumor detection and classification from 2015 to 2020. Comprehensive MRI methodology has been presented. Multiple performance degrading factors in MRI acquisition, datasets and CNN models exists. An all in one model / algorithm is the need of the future. Transfer learning and fine tuning can be an optimistic approach for opening the black box of deep learning. Abstract: During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its ability of handling huge amount of data. Its potential and ability have also been applied and tested in the detection of brain tumor using MRI images for effective prognosis and has shown remarkable performance. The main objective of this research work is to present a detailed critical analysis of the research and findings already done to detect and classify brain tumor through MRI images in the recent past. This analysis is specifically beneficial for the researchers who are experts of deep learning and are interested to apply their expertise for brain tumor detection and classification. As a first step, a brief review of the past research papers using Deep Learning for brain tumor classification and detection is carried out. Afterwards, a critical analysis of Deep Learning techniques proposed in these research papers (2015–2020) is being carried out in the form of a Table. Finally, the conclusion highlights the merits and demerits of deep neural networks. The results formulated in this paper will provide a thorough comparison of recent studies to the future researchers, along with the idea of the effectiveness of various deep learning approaches. We are confident that this study would greatly assist in advancement of brain tumor research.