Research Article
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Year 2023, Issue: 054, 296 - 306, 30.09.2023

Beyza Nur Tüzün Durmuş Özdemir

https://doi.org/10.59313/jsr-a.1293119

Abstract

References

  • [1] Copeland, B. J., and Proudfoot, D. (2007). Artificial intelligence. Philosophy of Psychology and Cognitive Science, 429–482. https://doi.org/10.1016/b978-044451540-7/50032-3
  • [2] Macukow, B. (2016). Neural Networks – State of Art, Brief History, Basic Models and Architecture. Computer Information Systems and Industrial Management, 3–14. https://doi.org/10.1007/978-3-319-45378-1_1
  • [3] Seyyarer, E., Uçkan, T., Hark, C., Ayata, F., İnan, M., and Karcı, A. (2019). Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). https://doi.org/10.1109/idap.2019.8875929
  • [4] Kartal, M., and Duman, O. (2019). Ship Detection from Optical Satellite Images with Deep Learning. 2019 9th International Conference on Recent Advances in Space Technologies (RAST). https://doi.org/10.1109/rast.2019.8767844
  • [5] Şeker, A., Diri, B., and Balık, H. H. (2017). Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47–64. Retrieved from https://dergipark.org.tr/tr/pub/gmbd/issue/31064/372661
  • [6] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Fadhel, M. A., Al-Amidie, M., and Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
  • [7] Özdemir, D., and Arslan, N. N. (2022). Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 628–640. https://doi.org/10.29130/dubited.976118
  • [8] Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., and Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/jbhi.2016.2636665
  • [9] Adeli, E., Rekik, I., Park, S. H., and Shen, D. (2020). Editorial: Predictive Intelligence in Biomedical and Health Informatics. IEEE Journal of Biomedical and Health Informatics, 24(2), 333–335. https://doi.org/10.1109/jbhi.2019.2962852
  • [10] Kumamaru, K. K., Machitori, A., Koba, R., Ijichi, S., Nakajima, Y., and Aoki, S. (2018). Global and Japanese regional variations in radiologist potential workload for computed tomography and magnetic resonance imaging examinations. Japanese Journal of Radiology, 36(4), 273–281. https://doi.org/10.1007/s11604-018-0724-5
  • [11] Yang, W., and Liu, J. (2013). Research and development of medical image fusion. https://doi.org/10.1109/icmipe.2013.6864557
  • [12] Srikanth, B., and Venkata Suryanarayana, S. (2021). Multi-Class classification of brain tumor images using data augmentation with deep neural network. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.601
  • [13] Lavanyadevi, R., Machakowsalya, M., Nivethitha, J., and Kumar, A. N. (2017). Brain tumor classification and segmentation in MRI images using PNN. 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). https://doi.org/10.1109/iceice.2017.8191888
  • [14] Julià-Sapé, M., Griffiths, J. R., Tate, R. A., Howe, F. A., Acosta, D., Postma, G., Underwood J., Majós C., and Arús, C. (2015). Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR in Biomedicine, 28(12), 1772–1787. https://doi.org/10.1002/nbm.3439
  • [15] Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., Soffietti, R., von Deimling, A., and Ellison, D. W. (2021). The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology, 23(8). https://doi.org/10.1093/neuonc/noab106
  • [16] Villa, C., Miquel, C., Mosses, D., Bernier, M., and Di Stefano, A. L. (2018). The 2016 World Health Organization classification of tumours of the central nervous system. La Presse Médicale, 47(11-12), e187–e200. https://doi.org/10.1016/j.lpm.2018.04.015
  • [17] Zhou, Z., Wu, S., Chang, K.-J., Chen, W.-R., Chen, Y.-S., Kuo, W.-H., Lin, C.-C., and Tsui, P.-H. (2015). Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features. Journal of Medical and Biological Engineering, 35(2), 178–187. https://doi.org/10.1007/s40846-015-0031-x
  • [18] Gürkahraman, K., and Karakış, R. (2021). Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(2), 997–1012. https://doi.org/10.17341/gazimmfd.762056
  • [19] Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., and Shoaib, M. (2020). A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor. IEEE Access, 8, 55135–55144. https://doi.org/10.1109/access.2020.2978629
  • [20] Sultan, H. H., Salem, N. M., and Al-Atabany, W. (2019). Multi-Classification of Brain Tumor Images Using Deep Neural Network. IEEE Access, 7, 69215–69225. https://doi.org/10.1109/access.2019.2919122
  • [21] Yerukalareddy, D. R., and Pavlovskiy, E. N. (2021). Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model. 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), 380–384. IEEE.
  • [22] Divya, S., Padma Suresh, L., and John, A. (2020). A Deep Transfer Learning framework for Multi Class Brain Tumor Classification using MRI. 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). https://doi.org/10.1109/icacccn51052.2020.9362908.
  • [23] Rehman, A., Naz, S., Razzak, M. I., Akram, F., and Imran, M. (2019). A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Systems, and Signal Processing, 39(2), 757–775. https://doi.org/10.1007/s00034-019-01246-3
  • [24] Deepak, S., and Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345. https://doi.org/10.1016/j.compbiomed.2019.103345
  • [25] Cheng, J. (2017, April 2). brain tumor dataset. Retrieved from Figshare website: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427
  • [26] Nickparvar, M. (2021). Brain Tumor MRI Dataset. Retrieved from Kaggle website: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/metadata
  • [27] Luque, A., Carrasco, A., Martín, A., and de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023

CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS

Year 2023, Issue: 054, 296 - 306, 30.09.2023

Beyza Nur Tüzün Durmuş Özdemir

https://doi.org/10.59313/jsr-a.1293119

Abstract

This study aims to present a comparative analysis of existing (state-of-the-art) deep learning models to identify early detection of brain tumor disease using MRI (Magnetic Resonance Imaging) images. For this purpose, GoogleNet, Mobilenetv2, InceptionV3, and Efficientnet-b0 deep learning models were coded on the Matlab platform and used to detect and classify brain tumor disease. Classification has been carried out on the common Glioma, Meningioma, and Pituitary brain tumors. The dataset includes 7022 brain MRI images in four different classes, which are shared publicly on the Kaggle platform. The dataset was pre-processed and the models were fine-tuned, and appropriate parameter values were used. When the statistical analysis results of the deep learning models we compared were evaluated, the results of Efficientnet-b0 (%99.54), InceptionV3 (%99.47), Mobilenetv2 (%98.93), and GoogleNet (%98.25) were obtained, in the order of success. The study results are predicted to be useful in offering suggestions to medical doctors and researchers in the relevant field in their decision-making processes. In particular, it offers some advantages regarding early diagnosis of the disease, shortening the diagnosis time, and minimizing human-induced errors.

Keywords

Deep Learning Brain Tumor Detection Image Classification MRI processing

References

  • [1] Copeland, B. J., and Proudfoot, D. (2007). Artificial intelligence. Philosophy of Psychology and Cognitive Science, 429–482. https://doi.org/10.1016/b978-044451540-7/50032-3
  • [2] Macukow, B. (2016). Neural Networks – State of Art, Brief History, Basic Models and Architecture. Computer Information Systems and Industrial Management, 3–14. https://doi.org/10.1007/978-3-319-45378-1_1
  • [3] Seyyarer, E., Uçkan, T., Hark, C., Ayata, F., İnan, M., and Karcı, A. (2019). Applications and Comparisons of Optimization Algorithms Used in Convolutional Neural Networks. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). https://doi.org/10.1109/idap.2019.8875929
  • [4] Kartal, M., and Duman, O. (2019). Ship Detection from Optical Satellite Images with Deep Learning. 2019 9th International Conference on Recent Advances in Space Technologies (RAST). https://doi.org/10.1109/rast.2019.8767844
  • [5] Şeker, A., Diri, B., and Balık, H. H. (2017). Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47–64. Retrieved from https://dergipark.org.tr/tr/pub/gmbd/issue/31064/372661
  • [6] Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Fadhel, M. A., Al-Amidie, M., and Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
  • [7] Özdemir, D., and Arslan, N. N. (2022). Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 628–640. https://doi.org/10.29130/dubited.976118
  • [8] Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., and Yang, G.-Z. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/jbhi.2016.2636665
  • [9] Adeli, E., Rekik, I., Park, S. H., and Shen, D. (2020). Editorial: Predictive Intelligence in Biomedical and Health Informatics. IEEE Journal of Biomedical and Health Informatics, 24(2), 333–335. https://doi.org/10.1109/jbhi.2019.2962852
  • [10] Kumamaru, K. K., Machitori, A., Koba, R., Ijichi, S., Nakajima, Y., and Aoki, S. (2018). Global and Japanese regional variations in radiologist potential workload for computed tomography and magnetic resonance imaging examinations. Japanese Journal of Radiology, 36(4), 273–281. https://doi.org/10.1007/s11604-018-0724-5
  • [11] Yang, W., and Liu, J. (2013). Research and development of medical image fusion. https://doi.org/10.1109/icmipe.2013.6864557
  • [12] Srikanth, B., and Venkata Suryanarayana, S. (2021). Multi-Class classification of brain tumor images using data augmentation with deep neural network. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.601
  • [13] Lavanyadevi, R., Machakowsalya, M., Nivethitha, J., and Kumar, A. N. (2017). Brain tumor classification and segmentation in MRI images using PNN. 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). https://doi.org/10.1109/iceice.2017.8191888
  • [14] Julià-Sapé, M., Griffiths, J. R., Tate, R. A., Howe, F. A., Acosta, D., Postma, G., Underwood J., Majós C., and Arús, C. (2015). Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes. NMR in Biomedicine, 28(12), 1772–1787. https://doi.org/10.1002/nbm.3439
  • [15] Louis, D. N., Perry, A., Wesseling, P., Brat, D. J., Cree, I. A., Figarella-Branger, D., Hawkins, C., Ng, H. K., Pfister, S. M., Reifenberger, G., Soffietti, R., von Deimling, A., and Ellison, D. W. (2021). The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology, 23(8). https://doi.org/10.1093/neuonc/noab106
  • [16] Villa, C., Miquel, C., Mosses, D., Bernier, M., and Di Stefano, A. L. (2018). The 2016 World Health Organization classification of tumours of the central nervous system. La Presse Médicale, 47(11-12), e187–e200. https://doi.org/10.1016/j.lpm.2018.04.015
  • [17] Zhou, Z., Wu, S., Chang, K.-J., Chen, W.-R., Chen, Y.-S., Kuo, W.-H., Lin, C.-C., and Tsui, P.-H. (2015). Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features. Journal of Medical and Biological Engineering, 35(2), 178–187. https://doi.org/10.1007/s40846-015-0031-x
  • [18] Gürkahraman, K., and Karakış, R. (2021). Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. Journal of the Faculty of Engineering and Architecture of Gazi University, 36(2), 997–1012. https://doi.org/10.17341/gazimmfd.762056
  • [19] Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., and Shoaib, M. (2020). A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor. IEEE Access, 8, 55135–55144. https://doi.org/10.1109/access.2020.2978629
  • [20] Sultan, H. H., Salem, N. M., and Al-Atabany, W. (2019). Multi-Classification of Brain Tumor Images Using Deep Neural Network. IEEE Access, 7, 69215–69225. https://doi.org/10.1109/access.2019.2919122
  • [21] Yerukalareddy, D. R., and Pavlovskiy, E. N. (2021). Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model. 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB), 380–384. IEEE.
  • [22] Divya, S., Padma Suresh, L., and John, A. (2020). A Deep Transfer Learning framework for Multi Class Brain Tumor Classification using MRI. 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). https://doi.org/10.1109/icacccn51052.2020.9362908.
  • [23] Rehman, A., Naz, S., Razzak, M. I., Akram, F., and Imran, M. (2019). A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning. Circuits, Systems, and Signal Processing, 39(2), 757–775. https://doi.org/10.1007/s00034-019-01246-3
  • [24] Deepak, S., and Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111, 103345. https://doi.org/10.1016/j.compbiomed.2019.103345
  • [25] Cheng, J. (2017, April 2). brain tumor dataset. Retrieved from Figshare website: https://figshare.com/articles/dataset/brain_tumor_dataset/1512427
  • [26] Nickparvar, M. (2021). Brain Tumor MRI Dataset. Retrieved from Kaggle website: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/metadata
  • [27] Luque, A., Carrasco, A., Martín, A., and de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023
There are 27 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Beyza Nur Tüzün 0000-0002-8289-6263

Durmuş Özdemir 0000-0002-9543-4076

Publication Date September 30, 2023
Submission Date May 5, 2023
Published in Issue Year 2023 Issue: 054

Cite

IEEE B. N. Tüzün and D. Özdemir, “CLASSIFICATION OF BRAIN TUMORS WITH DEEP LEARNING MODELS”, JSR-A, no. 054, pp. 296–306, September 2023, doi: 10.59313/jsr-a.1293119.
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