TY - JOUR
T1 - Early tumor diagnosis in brain MR images via deep convolutional neural network model
AU - Das, Tapan Kumar
AU - Roy, Pradeep Kumar
AU - Uddin, Mohy
AU - Srinivasan, Kathiravan
AU - Chang, Chuan Yu
AU - Syed-Abdul, Shabbir
N1 - Funding Information:
Acknowledgement: This work was supported by the under Grant: MOST 103-2221-E-224-016-MY3.
Funding Information:
Funding Statement: This research was partially funded by the “Intelligent Recognition Industry Service Research Center” from “The Featured Areas Research Center Program within the frame-work” of the “Higher Education Sprout Project” by the Ministry of Education (MOE) in Taiwan and the APC was funded by the aforementioned Project.
Publisher Copyright:
© 2021 Tech Science Press. All rights reserved.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert's inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy. This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor. The system runs on Tensor flow and uses a feature extraction module in Deep- CNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image. The results of this study showed that our model did not have any adverse effect on classification, achieved higher accuracy than the peers in recent years, and attained good detection outcomes including case of abnormality. In the future work, further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.
AB - Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert's inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy. This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor. The system runs on Tensor flow and uses a feature extraction module in Deep- CNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image. The results of this study showed that our model did not have any adverse effect on classification, achieved higher accuracy than the peers in recent years, and attained good detection outcomes including case of abnormality. In the future work, further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.
KW - Brain tumor magnetic resonance imaging
KW - Convolutional neural network
KW - Deep learning
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U2 - 10.32604/cmc.2021.016698
DO - 10.32604/cmc.2021.016698
M3 - Article
AN - SCOPUS:85104846625
SN - 1546-2218
VL - 68
SP - 2413
EP - 2429
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 2
ER -