Open Access
Algorithms, volume 16, issue 4, pages 176
A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
Publication type: Journal Article
Publication date: 2023-03-23
Computational Mathematics
Computational Theory and Mathematics
Theoretical Computer Science
Numerical Analysis
Abstract
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models.
Top-30
Citations by journals
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Citations by publishers
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IEEE
27 publications, 43.55%
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Springer Nature
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IGI Global
1 publication, 1.61%
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Research Square Platform LLC
1 publication, 1.61%
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Taylor & Francis
1 publication, 1.61%
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Mahmud M. I., Mamun M., Abdelgawad A. A. A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks // Algorithms. 2023. Vol. 16. No. 4. p. 176.
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Mahmud M. I., Mamun M., Abdelgawad A. A. A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks // Algorithms. 2023. Vol. 16. No. 4. p. 176.
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TY - JOUR
DO - 10.3390/a16040176
UR - https://doi.org/10.3390/a16040176
TI - A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
T2 - Algorithms
AU - Mahmud, Md Ishtyaq
AU - Mamun, Muntasir
AU - Abdelgawad, Ahmed Adel
PY - 2023
DA - 2023/03/23 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 176
IS - 4
VL - 16
SN - 1999-4893
SN - 1999-4893
ER -
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@article{2023_Mahmud,
author = {Md Ishtyaq Mahmud and Muntasir Mamun and Ahmed Adel Abdelgawad},
title = {A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks},
journal = {Algorithms},
year = {2023},
volume = {16},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {mar},
url = {https://doi.org/10.3390/a16040176},
number = {4},
pages = {176},
doi = {10.3390/a16040176}
}
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MLA
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Mahmud, Md Ishtyaq, et al. “A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks.” Algorithms, vol. 16, no. 4, Mar. 2023, p. 176. https://doi.org/10.3390/a16040176.
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