Cargando…
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques
BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two dee...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872362/ https://www.ncbi.nlm.nih.gov/pubmed/36691030 http://dx.doi.org/10.1186/s12911-023-02114-6 |
_version_ | 1784877387583848448 |
---|---|
author | Saeedi, Soheila Rezayi, Sorayya Keshavarz, Hamidreza R. Niakan Kalhori, Sharareh |
author_facet | Saeedi, Soheila Rezayi, Sorayya Keshavarz, Hamidreza R. Niakan Kalhori, Sharareh |
author_sort | Saeedi, Soheila |
collection | PubMed |
description | BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. MATERIALS AND METHODS: A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. RESULTS: The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05). CONCLUSION: The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection. |
format | Online Article Text |
id | pubmed-9872362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98723622023-01-25 MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques Saeedi, Soheila Rezayi, Sorayya Keshavarz, Hamidreza R. Niakan Kalhori, Sharareh BMC Med Inform Decis Mak Research BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. MATERIALS AND METHODS: A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. RESULTS: The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05). CONCLUSION: The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection. BioMed Central 2023-01-23 /pmc/articles/PMC9872362/ /pubmed/36691030 http://dx.doi.org/10.1186/s12911-023-02114-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Saeedi, Soheila Rezayi, Sorayya Keshavarz, Hamidreza R. Niakan Kalhori, Sharareh MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title | MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title_full | MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title_fullStr | MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title_full_unstemmed | MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title_short | MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
title_sort | mri-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872362/ https://www.ncbi.nlm.nih.gov/pubmed/36691030 http://dx.doi.org/10.1186/s12911-023-02114-6 |
work_keys_str_mv | AT saeedisoheila mribasedbraintumordetectionusingconvolutionaldeeplearningmethodsandchosenmachinelearningtechniques AT rezayisorayya mribasedbraintumordetectionusingconvolutionaldeeplearningmethodsandchosenmachinelearningtechniques AT keshavarzhamidreza mribasedbraintumordetectionusingconvolutionaldeeplearningmethodsandchosenmachinelearningtechniques AT rniakankalhorisharareh mribasedbraintumordetectionusingconvolutionaldeeplearningmethodsandchosenmachinelearningtechniques |