Authors :
Abhijeet A. Gajre; Omkar S. Khaladkar; Abhijit J. Patil
Volume/Issue :
Volume 7 - 2022, Issue 9 - September
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3eq8xwz
DOI :
https://doi.org/10.5281/zenodo.7227107
Abstract :
During the last decade, Computer Vision and
Artificial Intelligence (A.I) have transformed the world in
every way possible. Deep Learning is a subfield of
machine learning that has shown extraordinary results in
every field, particularly the biomedical field, due to its
proficiency in handling huge amounts of data. Its
potential and ability have also been applied and examined
in detecting brain tumors’ using MRI images for effective
prognosis and have shown impressive performance. The
main objective of this research is to present a detailed
fundamental analysis of the research and findings
performed to detect and classify brain tumors through
MRI images in the recent past. This analysis is especially
beneficial for researchers who are deep learning
connoisseurs and enthusiastic about applying their
expertise to brain tumor detection and classification. A
brief review of the past research papers using Deep
Learning for brain tumor classification and detection is
carried out as a first step. Afterwards, a critical analysis
of Deep Learning techniques like Transfer Learning,
Classic Neural Networks, Convolution Neural Networks,
etc., are proposed in these research papers and are being
carried out in the form of a graph. Ultimately, the
conclusion highlights the merits and demerits of deep
neural networks. The outcomes formulated in this paper
will deliver a thorough comparison of recent studies to
future researchers and the effectiveness of numerous deep
learning approaches. We are optimistic that this study
would extensively assist in advancing and improving
brain tumor research.
Keywords :
Brain Tumor, Deep Learning, Transfer Learning, Magnetic Resonance Imaging (MRI).
During the last decade, Computer Vision and
Artificial Intelligence (A.I) have transformed the world in
every way possible. Deep Learning is a subfield of
machine learning that has shown extraordinary results in
every field, particularly the biomedical field, due to its
proficiency in handling huge amounts of data. Its
potential and ability have also been applied and examined
in detecting brain tumors’ using MRI images for effective
prognosis and have shown impressive performance. The
main objective of this research is to present a detailed
fundamental analysis of the research and findings
performed to detect and classify brain tumors through
MRI images in the recent past. This analysis is especially
beneficial for researchers who are deep learning
connoisseurs and enthusiastic about applying their
expertise to brain tumor detection and classification. A
brief review of the past research papers using Deep
Learning for brain tumor classification and detection is
carried out as a first step. Afterwards, a critical analysis
of Deep Learning techniques like Transfer Learning,
Classic Neural Networks, Convolution Neural Networks,
etc., are proposed in these research papers and are being
carried out in the form of a graph. Ultimately, the
conclusion highlights the merits and demerits of deep
neural networks. The outcomes formulated in this paper
will deliver a thorough comparison of recent studies to
future researchers and the effectiveness of numerous deep
learning approaches. We are optimistic that this study
would extensively assist in advancing and improving
brain tumor research.
Keywords :
Brain Tumor, Deep Learning, Transfer Learning, Magnetic Resonance Imaging (MRI).
