Research Article
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Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning

Year 2023, Volume: 27 Issue: 3, 550 - 563, 30.06.2023

Emine Cengil Yeşim Eroğlu Ahmet Çınar Muhammed Yıldırım

https://doi.org/10.16984/saufenbilder.1067061

Abstract

Brain tumors are common tumors arising from parenchymal cells in the brain and the membranes that surround the brain. The most common brain tumors are glioma and meningioma. They can be benign or malignant. Treatment modalities such as surgery and radiotherapy are applied in malignant tumors. Tumors may be very small in the early stages and may be missed by showing findings similar to normal brain parenchyma. The correct determination of the localization of the tumor and its neighborhood with the surrounding vital tissues contributes to the determination of the treatment algorithm. In this paper, we aim to determine the classification and localization of gliomas originating from the parenchymal cells of the brain and meningiomas originating from the membranes surrounding the brain in brain magnetic resonance images using artificial intelligence methods. At first, the two classes of meningioma and glioma tumors of interest are selected in a public dataset. Relevant tumors are then labeled with the object labeling tool. The resulting labeled data is passed through the EfficientNet for feature extraction. Then Path Aggregation Network (PANet) is examined to generate the feature pyramid. Finally, object detection is performed using the detection layer of the You Only Look Once (YOLO) algorithm. The performance of the suggested method is shown with precision, recall and mean Average Precision (mAP) performance metrics. The values obtained are 0.885, 1.0, and 0.856, respectively. In the presented study, meningioma, and glioma, are automatically detected. The results demonstrate that using the proposed method will benefit medical people.

Keywords

: MRI brain tumor detection deep learning

References

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  • B. Garzín, K. E. Emblem, K. Mouridsen, B. Nedregaard, P. Due-Tønnessen, T. Nome, J. K. Hald, A. Bjørnerud, A. K. Håberg, Y. Kvinnsland, “Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction,” Acta radiologica, vol. 52 no. 9, pp. 1052-1060, 2011.
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  • M. C. Mabray, R. F. Barajas, S. Cha, “Modern brain tumor imaging,” Brain tumor research and treatment, vol. 3, no. 1, pp. 8-23, 2015.
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  • V. V. Kumar, P. G. K. Prince, “Deep belief network Assisted quadratic logit boost classifier for brain tumor detection using MR images.” Biomedical Signal Processing and Control, 81, 104415, 2023.
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  • G. Ramkumar, R. T. Prabu, N. P. Singh, U. Maheswaran, “WITHDRAWN: Experimental analysis of brain tumor detection system using Machine learning approach.” Materials Today: Proceedings, 2021.
  • V. Sabitha, J. Nayak, P. R. Reddy, “MRI brain tumor detection and classification using KPCA and KSVM.” Materials Today: Proceedings, 2021.
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  • F. Keller, “Computational Foundations of Cognitive Science.” Reading, 2: 2, 2010.
  • E. Cengil, A. Çınar, “A new approach for image classification: convolutional neural network.” European Journal of Technique (EJT), vol. 6, no. 2, pp. 96-103, 2016.
  • A. D. Jagtap, K. Kawaguchi, G. E. Karniadakis, "Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. "Journal of Computational Physics 404 : 109136, 2020.
  • E. Zisselman, A. Adler, M. Elad, “Compressed learning for image classification: A deep neural network approach.” In Handbook of Numerical Analysis, Elsevier. Vol. 19, pp. 3-17, 2018.
  • A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks.” Communications of the ACM, vol. 60, no.6, pp. 84-90, 2017.
  • K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556, 2014.
  • H. Kaiming, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition." İn Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
  • M. Tan, Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks.” in international Conference on Machine Learning, PMLR, 2019.
  • G. Marques, D. Agarwal, I. de la Torre Díez, “Automated medical diagnosis of COVID-19 through Efficient Net convolutional neural network.” Applied soft computing, 96, 106691, 2020.
  • J. Wang, Q. Liu, H. Xie, Z.Yang, H. Zhou, "Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks." Cancers, vol. 13, no. 4, pp. 661, 2021.
  • N. K. Chowdhury, M. A. Kabir, M. Rahman, N. Rezoana, "Ecovnet: An ensemble of deep convolutional neural networks based on efficientnet to detect covid-19 from chest x-rays." arXiv preprint arXiv:2009.11850, 2020.
  • K. Wang, J. H. Liew, Y. Zou, D. Zhou, J. Feng, “Panet: Few-shot image semantic segmentation with prototype alignment.” In proceedings of the IEEE/CVF international conference on computer vision, pp. 9197-9206, 2019.
  • J. Redmon, A. Farhadi, “Yolov3: An incremental improvement.” arXiv preprint arXiv:1804.02767, 2018.
  • A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection.” arXiv preprint arXiv:2004.10934, 2020.
  • E. Cengil, A. Çınar, “Poisonous Mushroom Detection using YOLOV5.” Turkish Journal of Science and Technology, vol. 6, no. 1, pp. 119-127, 2021.
  • C. H. Suh, H. S. Kim, S. C. Jung, C. G. Choi, S. J. Kim, “Clinically Relevant Imaging Features for MGMT Promoter Methylation in Multiple Glioblastoma Studies: A Systematic Review and Meta-Analysis.” American Journal of Neuroradiology, vol 39, no. 8: 1439, 2018
  • B. Tamrazi, M. S. Shiroishi, C. S. Liu, “Advanced Imaging of Intracranial Meningiomas.” Neurosurgery clinics of North America, vol. 27, no. 2, pp. 137-43, 2016.

Year 2023, Volume: 27 Issue: 3, 550 - 563, 30.06.2023

Emine Cengil Yeşim Eroğlu Ahmet Çınar Muhammed Yıldırım

https://doi.org/10.16984/saufenbilder.1067061

Abstract

References

  • J. M. Hempel, C. Brendle, B. Bender, G. Bier, M. Skardelly, I. Gepfner-Tuma, J. Schittenhelm, “Contrast enhancement predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study,” Journal of neuro-oncology, vol. 139, no.2, pp. 373-381, 2018.
  • J. Howard, Central Nervous System Tumors. Neurology Video Textbook DVD, C., Demos and Medical.
  • T. Ong, A. Bharatha, R. Alsufayan, S. Das, A. W. Lin, “MRI predictors for brain invasion in meningiomas,” The Neuroradiology Journal, vol. 34, no.1, pp.3-7, 2021.
  • B. Garzín, K. E. Emblem, K. Mouridsen, B. Nedregaard, P. Due-Tønnessen, T. Nome, J. K. Hald, A. Bjørnerud, A. K. Håberg, Y. Kvinnsland, “Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction,” Acta radiologica, vol. 52 no. 9, pp. 1052-1060, 2011.
  • C. J. Belden, P. A. Valdes, C. Ran, d. A. Pastel, B. T. Harris, C. E. Fadul, M. A. Israel, K. Paulsen, D. W. Roberts, “Genetics of glioblastoma: a window into its imaging and histopathologic variability,” Radiographics, vol. 31, no. 6, pp.1717-1740, 2011.
  • M. A. Baig, J. P. Klein, L. L. Mechtler, “Imaging of brain tumors,” CONTINUUM: Lifelong Learning in Neurology, vol. 22, no. 5, pp. 1529-1552, 2016.
  • M. C. Mabray, R. F. Barajas, S. Cha, “Modern brain tumor imaging,” Brain tumor research and treatment, vol. 3, no. 1, pp. 8-23, 2015.
  • E. Cengil, Brain tumor detection dataset [online], Available: https://github.com/ecengil/Brain-tumor-detection-dataset.
  • G. Garg, R. Garg, “Brain Tumor Detection and Classification based on Hybrid Ensemble Classifier.” arXiv preprint arXiv:2101.00216, 2021.
  • V. V. Kumar, P. G. K. Prince, “Deep belief network Assisted quadratic logit boost classifier for brain tumor detection using MR images.” Biomedical Signal Processing and Control, 81, 104415, 2023.
  • N. Kesav, M. Jibukumar, “Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN.” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 6229-6242, 2021.
  • M. F. Khan, P. Khatri, S. Lenka, D. Anuhya, A. Sanyal. “Detection of Brain Tumor from the MRI Images using Deep Hybrid Boosted based on Ensemble Techniques,” in 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) , 2022, pp. 1464-1467.
  • D. Rammurthy, P. Mahesh, “Whale Harris Hawks optimization based deep learning classifier for brain tumor detection using MRI images,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pp. 3259-3272, 2020.
  • D. R. Nayak, N. Padhy, P. K. Mallick, A. Singh, “A deep autoencoder approach for detection of brain tumor images,” Computers and Electrical Engineering, 102, 108238, 2022.
  • Q. Chuandong, L. Baosheng, H. Baole, “Fast brain tumor detection using adaptive stochastic gradient descent on shared-memory parallel environment,” Engineering Applications of Artificial Intelligence, 120, 105816, 2023.
  • S. Sangeeta, H. Nagendra. “Brain Tumor Detection and Classification Using Clustering and Comparison with FCM, ” in 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1-6, 2022.
  • M. Arif, F. Ajesh, S. Shamsudheen, O. Geman, D. Izdrui, D. Vicoveanu, “Brain tumor detection and classification by MRI using biologically inspired orthogonal wavelet transform and deep learning techniques.” Journal of Healthcare Engineering, 2022.
  • G. Ramkumar, R. T. Prabu, N. P. Singh, U. Maheswaran, “WITHDRAWN: Experimental analysis of brain tumor detection system using Machine learning approach.” Materials Today: Proceedings, 2021.
  • V. Sabitha, J. Nayak, P. R. Reddy, “MRI brain tumor detection and classification using KPCA and KSVM.” Materials Today: Proceedings, 2021.
  • M. Jian, X. Zhang, L. Ma, H. Yu, “Tumor detection in MRI brain images based on saliency computational modeling.” IFAC-PapersOnLine, vol. 53, no. 5, pp. 43-46, 2020.
  • M. K. Islam, M. S. Ali, M. S. Miah, M. M. Rahman, M.S Alam, M. A. Hossain, “Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm.” Machine Learning with Applications, 5, 100044, 2021.
  • SartajBhuvaji, Available: https://github.com/SartajBhuvaji/Brain-Tumor-Classification-DataSet/tree/master/Training.
  • tzutalin. labelImg. (2021). Available: https://github.com/tzutalin/labelImg.
  • F. Keller, “Computational Foundations of Cognitive Science.” Reading, 2: 2, 2010.
  • E. Cengil, A. Çınar, “A new approach for image classification: convolutional neural network.” European Journal of Technique (EJT), vol. 6, no. 2, pp. 96-103, 2016.
  • A. D. Jagtap, K. Kawaguchi, G. E. Karniadakis, "Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. "Journal of Computational Physics 404 : 109136, 2020.
  • E. Zisselman, A. Adler, M. Elad, “Compressed learning for image classification: A deep neural network approach.” In Handbook of Numerical Analysis, Elsevier. Vol. 19, pp. 3-17, 2018.
  • A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks.” Communications of the ACM, vol. 60, no.6, pp. 84-90, 2017.
  • K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556, 2014.
  • H. Kaiming, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition." İn Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
  • M. Tan, Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks.” in international Conference on Machine Learning, PMLR, 2019.
  • G. Marques, D. Agarwal, I. de la Torre Díez, “Automated medical diagnosis of COVID-19 through Efficient Net convolutional neural network.” Applied soft computing, 96, 106691, 2020.
  • J. Wang, Q. Liu, H. Xie, Z.Yang, H. Zhou, "Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks." Cancers, vol. 13, no. 4, pp. 661, 2021.
  • N. K. Chowdhury, M. A. Kabir, M. Rahman, N. Rezoana, "Ecovnet: An ensemble of deep convolutional neural networks based on efficientnet to detect covid-19 from chest x-rays." arXiv preprint arXiv:2009.11850, 2020.
  • K. Wang, J. H. Liew, Y. Zou, D. Zhou, J. Feng, “Panet: Few-shot image semantic segmentation with prototype alignment.” In proceedings of the IEEE/CVF international conference on computer vision, pp. 9197-9206, 2019.
  • J. Redmon, A. Farhadi, “Yolov3: An incremental improvement.” arXiv preprint arXiv:1804.02767, 2018.
  • A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection.” arXiv preprint arXiv:2004.10934, 2020.
  • E. Cengil, A. Çınar, “Poisonous Mushroom Detection using YOLOV5.” Turkish Journal of Science and Technology, vol. 6, no. 1, pp. 119-127, 2021.
  • C. H. Suh, H. S. Kim, S. C. Jung, C. G. Choi, S. J. Kim, “Clinically Relevant Imaging Features for MGMT Promoter Methylation in Multiple Glioblastoma Studies: A Systematic Review and Meta-Analysis.” American Journal of Neuroradiology, vol 39, no. 8: 1439, 2018
  • B. Tamrazi, M. S. Shiroishi, C. S. Liu, “Advanced Imaging of Intracranial Meningiomas.” Neurosurgery clinics of North America, vol. 27, no. 2, pp. 137-43, 2016.
There are 40 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Emine Cengil 0000-0003-4313-8694

Yeşim Eroğlu 0000-0003-3636-4810

Ahmet Çınar 0000-0001-5528-2226

Muhammed Yıldırım 0000-0003-1866-4721

Early Pub Date June 22, 2023
Publication Date June 30, 2023
Submission Date February 2, 2022
Acceptance Date March 2, 2023
Published in Issue Year 2023 Volume: 27 Issue: 3

Cite

APA Cengil, E., Eroğlu, Y., Çınar, A., Yıldırım, M. (2023). Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning. Sakarya University Journal of Science, 27(3), 550-563. https://doi.org/10.16984/saufenbilder.1067061
AMA Cengil E, Eroğlu Y, Çınar A, Yıldırım M. Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning. SAUJS. June 2023;27(3):550-563. doi:10.16984/saufenbilder.1067061
Chicago Cengil, Emine, Yeşim Eroğlu, Ahmet Çınar, and Muhammed Yıldırım. “Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images Using Deep Learning”. Sakarya University Journal of Science 27, no. 3 (June 2023): 550-63. https://doi.org/10.16984/saufenbilder.1067061.
EndNote Cengil E, Eroğlu Y, Çınar A, Yıldırım M (June 1, 2023) Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning. Sakarya University Journal of Science 27 3 550–563.
IEEE E. Cengil, Y. Eroğlu, A. Çınar, and M. Yıldırım, “Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning”, SAUJS, vol. 27, no. 3, pp. 550–563, 2023, doi: 10.16984/saufenbilder.1067061.
ISNAD Cengil, Emine et al. “Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images Using Deep Learning”. Sakarya University Journal of Science 27/3 (June 2023), 550-563. https://doi.org/10.16984/saufenbilder.1067061.
JAMA Cengil E, Eroğlu Y, Çınar A, Yıldırım M. Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning. SAUJS. 2023;27:550–563.
MLA Cengil, Emine et al. “Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images Using Deep Learning”. Sakarya University Journal of Science, vol. 27, no. 3, 2023, pp. 550-63, doi:10.16984/saufenbilder.1067061.
Vancouver Cengil E, Eroğlu Y, Çınar A, Yıldırım M. Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning. SAUJS. 2023;27(3):550-63.
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