Research Article
BibTex RIS Cite

Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach

Year 2023, Volume: 27 Issue: 5, 1128 - 1140, 18.10.2023

Abdullah Şener Burhan Ergen

https://doi.org/10.16984/saufenbilder.1302803

Abstract

Early detection and diagnosis of brain tumors have a critical impact on the treatment of brain tumor patients. This is because initiating interventions early directly impacts the patient's chances of continuing their life. In the field of medical research, various methods are employed for the detection of brain tumors. Among these methods, magnetic resonance imaging (MRI) is the most popular due to its superior image quality. By leveraging technological advancements, the utilization of deep learning techniques in the identification of brain tumors ensures both high accuracy and simplification of the process. In a conducted study, a new model was developed by utilizing the VGG-19 architecture, a popular convolutional neural network model, to achieve high accuracy in brain tumor detection. In the study, precision, F1 score, accuracy, specificity, Matthews correlation coefficient, and recall metrics were used to evaluate the performance of the developed model. The deep learning model developed for brain tumor detection was trained and evaluated on an open-source dataset consisting of MRI images of gliomas, meningiomas, pituitary tumors, and healthy brains. The results obtained from the study demonstrate the promising potential of using the developed model in clinical applications for brain tumor detection. The high accuracy achieved by the developed model emphasizes its potential as an auxiliary resource for healthcare professionals in brain tumor detection. This research aims to evaluate the model as a valuable tool that can assist physicians in making informed treatment decisions regarding brain tumor diagnosis.

Keywords

Brain tumor detection image classification VGG-19 architecture deep learning support vector machines.

References

  • M. Tanveer, M.A. Ganaie, I. Beheshti, T. Goel, N. Ahmad, K. T. Lai, C. T. Lin. “Deep learning for brain age estimation: A systematic review”. Information Fusion, 2023.
  • S. Solanki, U. P. Singh, S. S. Chouhan, S. Jain. “Brain Tumor Detection and Classification using Intelligence Techniques: An Overview”. IEEE Access, 2023.
  • S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi, J. Si. “Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images”. IEEE Access, vol. 10, pp. 34716-34730, 2022.
  • H. N. Kilinç, Y. Uzun. “Beyin Cerrahisi İçin Artırılmış Gerçeklik Uygulaması Gerçekleştirmek”. Avrupa Bilim ve Teknoloji Dergisi, vol. 33, pp. 290-296, 2022.
  • R. K. Gupta, S. Bharti, N. Kunhare, Y. Sahu, N. Pathik. “Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks”. Interdisciplinary Sciences: Computational Life Sciences, pp. 1-18, 2022.
  • Z. Zhou, Z. He, Y. Jia. “AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images”. Neurocomputing, vol. 402, pp. 235-244, 2020.
  • B. Kapusiz, Y. Uzun, S. Koçer, Ö. Dündar. “Brain Tumor Detection and Brain Tumor Area Calculation with Matlab”. Journal of Scientific Reports-A, vol. 052, pp. 352-364, 2023.
  • Dataset: : https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri Access Date: 05.07.2022.
  • A Tiwari., S. Srivastava, M. Pant. “Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, vol. 131, pp. 244-260, 2020.
  • K. Dağlı, O. Eroğul. “Classification of Brain Tumors via Deep Learning Models”. In 2020 Medical Technologies Congress (TIPTEKNO) (pp. 1-4). IEEE, 2020.
  • G. Mohan, M. M. Subashini. “MRI based medical image analysis: Survey on brain tumor grade classification”. Biomedical Signal Processing and Control, vol. 39, 139-161, 2018.
  • M. Cossio. “Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis”. arXiv preprint arXiv:2303.01178, 2023.
  • X. Liu, Z. Deng, Y. Yang. “Recent progress in semantic image segmentation”. Artificial Intelligence Reiew, vol. 52, pp. 1089-1106, 2019
  • K. Sharifani, M. Amini. “Machine Learning and Deep Learning: A Review of Methods and Applications”. World Information Technology and Engineering Journal, vol. 10, no. 07, pp. 3897-3904, 2023.
  • N. Kanwisher, M. Khosla, K. Dobs. “Using artificial neural networks to ask ‘why’questions of minds and brains”. Trends in Neurosciences, vol. 46, no. 3, pp. 240-254, 2023.
  • A. H. Marblestone, G. Wayne, K. P. Kording. “Toward an integration of deep learning and neuroscience”. Frontiers in computational neuroscience, 94, 2016.
  • S. Cong, Y. Zhou. “A review of convolutional neural network architectures and their optimizations”. Artificial Intelligence Review, vol. 56, no. 3, pp. 1905-1969, 2023.
  • J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Chen. “Recent advances in convolutional neural networks”. Pattern recognition, vol. 77, pp. 354-377, 2018.
  • S. Min, B. Lee, S. Yoon.“Deep learning in bioinformatics”. Briefings in bioinformatics, vol. 18, no. 5, pp. 851- 869, 2017.
  • M. Mirbabaie, S. Stieglitz, N. R. J. Frick. “Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction”. Health Technology (Berl) vol. 11, no. 4, pp. 693-731, 2021.
  • K. Hanbay. “Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform”. Journal of the Faculty of Engıneerıng and Architecture of Gazi Unıversity, vol. 35,no. 1, 443-456, 2020.
  • M. Niepert. M. Ahmed, K. Kutzkov. “Learning convolutional neural networks for graphs”. In International conference on machine learning, . Germany, vol. 2016. pp. 2014-2023, 2014.
  • F. Kurt. “Investigation of the Effect of Hyper Parameters in Neural Networks”. Ankara:Hacettepe University, 2018.
  • M. Toğaçar, B. Ergen, F. Özyurt. “Classification of Flower Images Using Feature Selection Methods in Convolutional Neural Network Models”. Fırat University Journal of Engineering Sciences, vol. 32, no. 1, pp. 47-56, 2020.
  • M. Mateen, J. Wen, S. Song, Z. Huang. “Fundus image classification using VGG-19 architecture with PCA and SVD”. Symmetry, vol. 11, no. 1, 1, 2018.
  • S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu. “Applications of support vector machine (SVM) learning in cancer genomics”. Cancer genomics & proteomics, vol. 15, no. 1, pp. 41-51, 2018.
  • F. Bulut. “Sınıflandırıcı topluluklarının dengesiz veri kümeleri üzerindeki performans analizleri”. Bilişim Teknolojileri Dergisi, 9(2), 153, 2016.
  • A. K. Sharma, A. Nandal, A. Dhaka, D. Koundal, D. C. Bogatinoska, H. Alyami .“Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection”. BioMed Research International, 2022.
  • J. S. Paul, A. J. Plassard, B. A. Landman, D. Fabbri. “Deep learning for brain tumor classification'” Proc. SPIE, vol. 10137, Art. no. 1013710, 2017.
  • M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, S. W. Baik. “Multi-grade brain tumor classification using deep CNN with extensive data augmentation” Journal of Computational Science, vol. 30, pp. pp. 174-182, 2019.
  • S. Kumar, A. Sharma, T. Tsunoda. “Brain wave classification using long short-term memory network based Optical predictor” Scientific Reports, vol. 9, no. 1, pp. 1-13, 2019.
  • S. Khawaldeh, U. Pervaiz, A. Rafiq, R. Alkhawaldeh. “Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks” Applied Science, vol. 8, no. 1, p. 27, 2017.
  • N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, T. R. Mengko. “Brain tumor classification using convolutional neural network” in Proc. World Congress on Medical Physics and Biomedical Engineering Singapore: Springer, pp. 183-189, 2018.
  • J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, Q. Feng. “Enhanced performance of brain tumor classification via tumor region augmentation and partition” PLoS ONE, vol. 10, no. 10, Art. no. e0140381, 2015.
  • P. Afshar, K. N. Plataniotis, A. Mohammadi. “Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries” in Proceedings IEEE International Conference of Acoustic, Speech and Signal Processing (ICASSP), pp. 1368-1372, 2019.
  • M. Soltaninejad, G. Yang, T. Lambrou, N. Allinson, T. L. Jones, T. R. Barrick, F. A. Howe, X. Ye. “Supervised learning based multimodal MRI brain tumor segmentation using texture features from supervoxels” Computer Methods Programs in Biomedicine, vol. 157, pp. 69-84, 2018.
  • M. Toğaçar, B. Ergen, Z. Cömert. “BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model”. Medical hypotheses, 134, 109531, 2020.
  • A. Anaya-Isaza, L. Mera-Jiménez. “Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging”. IEEE Access, vol. 10, pp. 23217-23233, 2022.
  • F. Bulut, İ. Kılıç, İ.F. İnce. “Comparison And Performance Analysis Of Image Segmentation Algorithms On Brain Tumor Detection”. Dokuz Eylül University Faculty of Engineering Science and Engineering Journal, 20(58), pp.173-186, 2018.

Year 2023, Volume: 27 Issue: 5, 1128 - 1140, 18.10.2023

Abdullah Şener Burhan Ergen

https://doi.org/10.16984/saufenbilder.1302803

Abstract

References

  • M. Tanveer, M.A. Ganaie, I. Beheshti, T. Goel, N. Ahmad, K. T. Lai, C. T. Lin. “Deep learning for brain age estimation: A systematic review”. Information Fusion, 2023.
  • S. Solanki, U. P. Singh, S. S. Chouhan, S. Jain. “Brain Tumor Detection and Classification using Intelligence Techniques: An Overview”. IEEE Access, 2023.
  • S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi, J. Si. “Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images”. IEEE Access, vol. 10, pp. 34716-34730, 2022.
  • H. N. Kilinç, Y. Uzun. “Beyin Cerrahisi İçin Artırılmış Gerçeklik Uygulaması Gerçekleştirmek”. Avrupa Bilim ve Teknoloji Dergisi, vol. 33, pp. 290-296, 2022.
  • R. K. Gupta, S. Bharti, N. Kunhare, Y. Sahu, N. Pathik. “Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks”. Interdisciplinary Sciences: Computational Life Sciences, pp. 1-18, 2022.
  • Z. Zhou, Z. He, Y. Jia. “AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images”. Neurocomputing, vol. 402, pp. 235-244, 2020.
  • B. Kapusiz, Y. Uzun, S. Koçer, Ö. Dündar. “Brain Tumor Detection and Brain Tumor Area Calculation with Matlab”. Journal of Scientific Reports-A, vol. 052, pp. 352-364, 2023.
  • Dataset: : https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri Access Date: 05.07.2022.
  • A Tiwari., S. Srivastava, M. Pant. “Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, vol. 131, pp. 244-260, 2020.
  • K. Dağlı, O. Eroğul. “Classification of Brain Tumors via Deep Learning Models”. In 2020 Medical Technologies Congress (TIPTEKNO) (pp. 1-4). IEEE, 2020.
  • G. Mohan, M. M. Subashini. “MRI based medical image analysis: Survey on brain tumor grade classification”. Biomedical Signal Processing and Control, vol. 39, 139-161, 2018.
  • M. Cossio. “Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis”. arXiv preprint arXiv:2303.01178, 2023.
  • X. Liu, Z. Deng, Y. Yang. “Recent progress in semantic image segmentation”. Artificial Intelligence Reiew, vol. 52, pp. 1089-1106, 2019
  • K. Sharifani, M. Amini. “Machine Learning and Deep Learning: A Review of Methods and Applications”. World Information Technology and Engineering Journal, vol. 10, no. 07, pp. 3897-3904, 2023.
  • N. Kanwisher, M. Khosla, K. Dobs. “Using artificial neural networks to ask ‘why’questions of minds and brains”. Trends in Neurosciences, vol. 46, no. 3, pp. 240-254, 2023.
  • A. H. Marblestone, G. Wayne, K. P. Kording. “Toward an integration of deep learning and neuroscience”. Frontiers in computational neuroscience, 94, 2016.
  • S. Cong, Y. Zhou. “A review of convolutional neural network architectures and their optimizations”. Artificial Intelligence Review, vol. 56, no. 3, pp. 1905-1969, 2023.
  • J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Chen. “Recent advances in convolutional neural networks”. Pattern recognition, vol. 77, pp. 354-377, 2018.
  • S. Min, B. Lee, S. Yoon.“Deep learning in bioinformatics”. Briefings in bioinformatics, vol. 18, no. 5, pp. 851- 869, 2017.
  • M. Mirbabaie, S. Stieglitz, N. R. J. Frick. “Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction”. Health Technology (Berl) vol. 11, no. 4, pp. 693-731, 2021.
  • K. Hanbay. “Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform”. Journal of the Faculty of Engıneerıng and Architecture of Gazi Unıversity, vol. 35,no. 1, 443-456, 2020.
  • M. Niepert. M. Ahmed, K. Kutzkov. “Learning convolutional neural networks for graphs”. In International conference on machine learning, . Germany, vol. 2016. pp. 2014-2023, 2014.
  • F. Kurt. “Investigation of the Effect of Hyper Parameters in Neural Networks”. Ankara:Hacettepe University, 2018.
  • M. Toğaçar, B. Ergen, F. Özyurt. “Classification of Flower Images Using Feature Selection Methods in Convolutional Neural Network Models”. Fırat University Journal of Engineering Sciences, vol. 32, no. 1, pp. 47-56, 2020.
  • M. Mateen, J. Wen, S. Song, Z. Huang. “Fundus image classification using VGG-19 architecture with PCA and SVD”. Symmetry, vol. 11, no. 1, 1, 2018.
  • S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu. “Applications of support vector machine (SVM) learning in cancer genomics”. Cancer genomics & proteomics, vol. 15, no. 1, pp. 41-51, 2018.
  • F. Bulut. “Sınıflandırıcı topluluklarının dengesiz veri kümeleri üzerindeki performans analizleri”. Bilişim Teknolojileri Dergisi, 9(2), 153, 2016.
  • A. K. Sharma, A. Nandal, A. Dhaka, D. Koundal, D. C. Bogatinoska, H. Alyami .“Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection”. BioMed Research International, 2022.
  • J. S. Paul, A. J. Plassard, B. A. Landman, D. Fabbri. “Deep learning for brain tumor classification'” Proc. SPIE, vol. 10137, Art. no. 1013710, 2017.
  • M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, S. W. Baik. “Multi-grade brain tumor classification using deep CNN with extensive data augmentation” Journal of Computational Science, vol. 30, pp. pp. 174-182, 2019.
  • S. Kumar, A. Sharma, T. Tsunoda. “Brain wave classification using long short-term memory network based Optical predictor” Scientific Reports, vol. 9, no. 1, pp. 1-13, 2019.
  • S. Khawaldeh, U. Pervaiz, A. Rafiq, R. Alkhawaldeh. “Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks” Applied Science, vol. 8, no. 1, p. 27, 2017.
  • N. Abiwinanda, M. Hanif, S. T. Hesaputra, A. Handayani, T. R. Mengko. “Brain tumor classification using convolutional neural network” in Proc. World Congress on Medical Physics and Biomedical Engineering Singapore: Springer, pp. 183-189, 2018.
  • J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, Q. Feng. “Enhanced performance of brain tumor classification via tumor region augmentation and partition” PLoS ONE, vol. 10, no. 10, Art. no. e0140381, 2015.
  • P. Afshar, K. N. Plataniotis, A. Mohammadi. “Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries” in Proceedings IEEE International Conference of Acoustic, Speech and Signal Processing (ICASSP), pp. 1368-1372, 2019.
  • M. Soltaninejad, G. Yang, T. Lambrou, N. Allinson, T. L. Jones, T. R. Barrick, F. A. Howe, X. Ye. “Supervised learning based multimodal MRI brain tumor segmentation using texture features from supervoxels” Computer Methods Programs in Biomedicine, vol. 157, pp. 69-84, 2018.
  • M. Toğaçar, B. Ergen, Z. Cömert. “BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model”. Medical hypotheses, 134, 109531, 2020.
  • A. Anaya-Isaza, L. Mera-Jiménez. “Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging”. IEEE Access, vol. 10, pp. 23217-23233, 2022.
  • F. Bulut, İ. Kılıç, İ.F. İnce. “Comparison And Performance Analysis Of Image Segmentation Algorithms On Brain Tumor Detection”. Dokuz Eylül University Faculty of Engineering Science and Engineering Journal, 20(58), pp.173-186, 2018.
There are 39 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Articles
Authors

Abdullah Şener 0000-0002-8927-5638

Burhan Ergen 0000-0003-3244-2615

Early Pub Date October 5, 2023
Publication Date October 18, 2023
Submission Date May 25, 2023
Acceptance Date September 18, 2023
Published in Issue Year 2023 Volume: 27 Issue: 5

Cite

APA Şener, A., & Ergen, B. (2023). Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach. Sakarya University Journal of Science, 27(5), 1128-1140. https://doi.org/10.16984/saufenbilder.1302803
AMA Şener A, Ergen B. Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach. SAUJS. October 2023;27(5):1128-1140. doi:10.16984/saufenbilder.1302803
Chicago Şener, Abdullah, and Burhan Ergen. “Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach”. Sakarya University Journal of Science 27, no. 5 (October 2023): 1128-40. https://doi.org/10.16984/saufenbilder.1302803.
EndNote Şener A, Ergen B (October 1, 2023) Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach. Sakarya University Journal of Science 27 5 1128–1140.
IEEE A. Şener and B. Ergen, “Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach”, SAUJS, vol. 27, no. 5, pp. 1128–1140, 2023, doi: 10.16984/saufenbilder.1302803.
ISNAD Şener, Abdullah - Ergen, Burhan. “Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach”. Sakarya University Journal of Science 27/5 (October 2023), 1128-1140. https://doi.org/10.16984/saufenbilder.1302803.
JAMA Şener A, Ergen B. Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach. SAUJS. 2023;27:1128–1140.
MLA Şener, Abdullah and Burhan Ergen. “Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach”. Sakarya University Journal of Science, vol. 27, no. 5, 2023, pp. 1128-40, doi:10.16984/saufenbilder.1302803.
Vancouver Şener A, Ergen B. Enhancing Brain Tumor Detection on MRI Images Using an Innovative VGG-19 Model-Based Approach. SAUJS. 2023;27(5):1128-40.
 Download Cover Image

30930  This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.