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BEYİN MR GÖRÜNTÜLERİNDEN TÜMÖR TESPİTİ İÇİN DERİN ÖĞRENMEYE DAYALI HİBRİT MODELLER

Year 2023, Volume: 26 Issue: 3, 718 - 733, 03.09.2023

İclal Özcan , Serkan Öztürk

https://doi.org/10.17780/ksujes.1293378

Abstract

İnsan hücrelerinin aşırı bölünmeye bağlı anormal bir şekilde çoğalmasına tümör denmektedir. Vücudun birçok noktasında oluşabilen tümörler, oluştuğu yere göre tehlikelilik derecesine sahiptir. Beyin, tümör oluşumunda en tehlikeli bölgelerden birisidir. Beyin bölgesindeki tümörlerin tespiti için son yıllarda yoğun çalışmalar gerçekleştirilmiştir. Yapay zeka temelli yöntemler bu çalışmaların başında gelmektedir. Bir derin öğrenme yöntemi olan evrişimli sinir ağları (CNN) sınıflandırma, özellik çıkarma ve transfer öğrenme amaçlarıyla kullanılmaktadır. Bu çalışmada CNN yöntemi beyin MR görüntülerinden özellik çıkarma amacıyla kullanılmıştır. Bu kapsamda ön eğitimli CNN modellerinden DarkNet53 modeli özellik çıkarımı için seçilmiştir. DarkNet53 modelinin özellik çıkarıcı katmanları sırasıyla conv52, res23, avg1 ve conv53 katmanlarıdır. Özellik çıkarımından sonra özellik seçimi işlemi uygulanmıştır. Relief ve Ki-Kare Test yöntemleri özellik seçici yöntemler olarak seçilmiştir. Özellik çıkarımından sonra klasik makine öğrenme yöntemlerinden birisi olan destek vektör makineleri algoritması sınıflandırıcı yöntem olarak belirlenmiştir. Önerilen yöntem, “Brain MRI Images for Brain Tumor Detection” veri seti üzerinde denenmiştir. Deneysel sonuçlara göre: res23 katmanının özellik çıkarıcı, Ki-Kare Test yönteminin özellik seçici olarak belirlendiği önerilen yöntemle en iyi sonuç elde edilmiştir.

Keywords

Beyin MR görüntüleri tümör tespiti derin özellikler özellik seçimi SVM

References

  • Amin, J., Sharif, M., Yasmin, M., & Fernandes, S.L. (2018). Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87,290–297. https://doi.org/10.1016/j.future.2018.04.065.
  • Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, 144–152. https://doi.org/10.1145/130385.130401.
  • Budak, H. (2018). Özellik seçim yöntemleri ve yeni bir yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22:21–31. DOI: 10.19113/sdufbed.01653.
  • Febrianto, D., Soesanti, I., & Nugroho, H. (2020). Convolutional neural network for brain tumor detection. IOP Conference Series: Materials Science and Engineering, volume 771, 012031, IOP Publishing. https://doi.org/10.1088/1757-899X/771/1/012031.
  • Fırat HAKVERDİ, (2019), Veri Önişleme. https://prezi.com/p/ vk31emxjhl4y/veri-on-isleme/, online; accessed 14 December 2022.
  • Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90. https://doi.org/10.1145/3065386.
  • Ozcan, T., & Basturk, A. (2021). Performance improvement of pre-trained convolutional neural networks for action recognition. The Computer Journal, 64(11), 1715-1730. https://doi.org/10.1093/comjnl/bxaa029
  • Liu, H., & Setiono, R. (1997). Feature selection via discretization. IEEE Transactions on knowledge and Data Engineering, 9(4), 642-645. https://doi.org/10.1109/69.617056.
  • Mathworks, (2019). Feature Extraction, https://www.mathworks.com/ discovery/feature-extraction.html, online; accessed 17 December 2022.
  • Mohsen, H., El-Dahshan, E., El-Horbaty, E., & Salem, A. (2017). Brain tumor type classification based on support vector machine in magnetic resonance images. Annals Of “Dunarea De Jos” University Of Galati, Mathematics, Physics, Theoretical mechanics, Fascicle II, Year IX (XL), 1.
  • Mohsen, H., El-Dahshan, E.S.A., El-Horbaty, E.S.M., & Salem, A.B.M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1):68–71. https://doi.org/10.1016/j.fcij.2017.12.001.
  • NAVONEEL CHAKRABARTY, (2018). Brain MRI Images for Brain Tumor Detection, https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection?resource=download, online; accessed 14 December 2022.
  • Oğuzhan Taş, (2016). Destek Vektör Makineleri, https://www.slideshare.net/oguzhantas/destek-vektr-makineleri-support-vector-machine, online; accessed 15 December 2022.
  • Pranitha, K., Vurukonda, N., & Nayak, R.K. (2022). A Comprehensive Survey on MRI Images Classification for Brain Tumor Identification using Deep Learning Techniques. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 1206–1212, IEEE. https://doi.org/10.1109/ICOSEC54921.2022.9951913.
  • Rai, H.M. & Chatterjee, K. (2021). 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimedia Tools and Applications, 80(28):36111–36141. https://doi.org/10.1007/s11042-021-11504-9 . Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-y.
  • Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., & Ahuja, C.K. (2011). Multiclass brain tumor classification using GA-SVM. 2011 Developments in E-systems Engineering, 182–187, IEEE. https://doi.org/10.1109/DeSE.2011.31.
  • Sahin, E. (2017). Özellik seçimi algoritmaları kullanılarak heyelanda etkili faktörlerin belirlenmesi ve heyelan duyarlılık haritalarının üretilmesi. İstanbul Teknik Üniversitesi Geomatik Mühendisliği Anabilim Dalı, Doktora Tezi, Doktora Tezi), İstanbul.
  • Seetha, J. & Raja, S.S. (2018). Brain tumor classification using convolutional neural networks. Biomedical & Pharmacology Journal, 11(3):1457-1461. https://dx.doi.org/10.13005/bpj/1511.
  • Siddique, M.A.B., Sakib, S., Khan, M.M.R., Tanzeem, A.K., Chowdhury, M., & Yasmin, N. (2020). Deep convolutional neural networks model-based brain tumor detection in brain MRI images. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 909–914, IEEE. https://doi.org/10.1109/I-SMAC49090.2020.9243461.
  • Tiwari, A., Srivastava, S., & Pant, M. (2020). Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131:244–260. https://doi.org/10.1016/j.patrec.2019.11.020.
  • Wikipedia, (2013). Relief (feature selection), https://en.wikipedia. org/wiki/Relief_(feature_selection), online; accessed 14 December 2022.
  • Xu, Y., Jia, Z., Ai, Y., Zhang, F., Lai, M., Eric, I., & Chang, C. (2015). Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), 947–951, IEEE. https://doi.org/10.1109/ICASSP.2015.7178109.
  • Zacharaki, E.I., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E.R., & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 62(6):1609–1618. https://doi.org/10.1002/mrm.22147.

DEEP LEARNING BASED HYBRID MODELS FOR TUMOR DETECTION FROM BRAIN MR IMAGES

Year 2023, Volume: 26 Issue: 3, 718 - 733, 03.09.2023

İclal Özcan , Serkan Öztürk

https://doi.org/10.17780/ksujes.1293378

Abstract

An abnormal proliferation of human cells due to excessive division is called a tumor. Tumors, which can form in many parts of the body, have a degree of danger according to where they occur. The brain is one of the most dangerous areas of tumor formation. Intense studies have been carried out in recent years for the detection of tumors in the brain region. Artificial intelligence-based methods are at the forefront of these studies. Convolutional neural networks (CNN), a deep learning method, are used for classification, feature extraction and transfer learning purposes. In this study, CNN method was used for feature extraction from brain MR images. In this context, DarkNet53 model, one of the pre-trained CNN models, was selected for feature extraction. The feature extractor layers of the DarkNet53 model are conv52, res23, avg1, and conv53, respectively. After feature extraction, feature selection process was applied. Relief and Chi-Square Test methods were chosen as feature-selective methods. After feature extraction, the support vector machine algorithm, which is one of the classical machine learning methods, was determined as the classifier method. The proposed method has been tested on the “Brain MRI Images for Brain Tumor Detection” dataset. According to the experimental results, the best result was obtained with the proposed method in which the res23 layer was determined as feature extractor and the Chi-Square Test method as feature selective.

Keywords

Brain MR images tumor detection deep features feature selection SVM

References

  • Amin, J., Sharif, M., Yasmin, M., & Fernandes, S.L. (2018). Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Generation Computer Systems, 87,290–297. https://doi.org/10.1016/j.future.2018.04.065.
  • Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, 144–152. https://doi.org/10.1145/130385.130401.
  • Budak, H. (2018). Özellik seçim yöntemleri ve yeni bir yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22:21–31. DOI: 10.19113/sdufbed.01653.
  • Febrianto, D., Soesanti, I., & Nugroho, H. (2020). Convolutional neural network for brain tumor detection. IOP Conference Series: Materials Science and Engineering, volume 771, 012031, IOP Publishing. https://doi.org/10.1088/1757-899X/771/1/012031.
  • Fırat HAKVERDİ, (2019), Veri Önişleme. https://prezi.com/p/ vk31emxjhl4y/veri-on-isleme/, online; accessed 14 December 2022.
  • Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90. https://doi.org/10.1145/3065386.
  • Ozcan, T., & Basturk, A. (2021). Performance improvement of pre-trained convolutional neural networks for action recognition. The Computer Journal, 64(11), 1715-1730. https://doi.org/10.1093/comjnl/bxaa029
  • Liu, H., & Setiono, R. (1997). Feature selection via discretization. IEEE Transactions on knowledge and Data Engineering, 9(4), 642-645. https://doi.org/10.1109/69.617056.
  • Mathworks, (2019). Feature Extraction, https://www.mathworks.com/ discovery/feature-extraction.html, online; accessed 17 December 2022.
  • Mohsen, H., El-Dahshan, E., El-Horbaty, E., & Salem, A. (2017). Brain tumor type classification based on support vector machine in magnetic resonance images. Annals Of “Dunarea De Jos” University Of Galati, Mathematics, Physics, Theoretical mechanics, Fascicle II, Year IX (XL), 1.
  • Mohsen, H., El-Dahshan, E.S.A., El-Horbaty, E.S.M., & Salem, A.B.M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1):68–71. https://doi.org/10.1016/j.fcij.2017.12.001.
  • NAVONEEL CHAKRABARTY, (2018). Brain MRI Images for Brain Tumor Detection, https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection?resource=download, online; accessed 14 December 2022.
  • Oğuzhan Taş, (2016). Destek Vektör Makineleri, https://www.slideshare.net/oguzhantas/destek-vektr-makineleri-support-vector-machine, online; accessed 15 December 2022.
  • Pranitha, K., Vurukonda, N., & Nayak, R.K. (2022). A Comprehensive Survey on MRI Images Classification for Brain Tumor Identification using Deep Learning Techniques. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 1206–1212, IEEE. https://doi.org/10.1109/ICOSEC54921.2022.9951913.
  • Rai, H.M. & Chatterjee, K. (2021). 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multimedia Tools and Applications, 80(28):36111–36141. https://doi.org/10.1007/s11042-021-11504-9 . Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-y.
  • Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., & Ahuja, C.K. (2011). Multiclass brain tumor classification using GA-SVM. 2011 Developments in E-systems Engineering, 182–187, IEEE. https://doi.org/10.1109/DeSE.2011.31.
  • Sahin, E. (2017). Özellik seçimi algoritmaları kullanılarak heyelanda etkili faktörlerin belirlenmesi ve heyelan duyarlılık haritalarının üretilmesi. İstanbul Teknik Üniversitesi Geomatik Mühendisliği Anabilim Dalı, Doktora Tezi, Doktora Tezi), İstanbul.
  • Seetha, J. & Raja, S.S. (2018). Brain tumor classification using convolutional neural networks. Biomedical & Pharmacology Journal, 11(3):1457-1461. https://dx.doi.org/10.13005/bpj/1511.
  • Siddique, M.A.B., Sakib, S., Khan, M.M.R., Tanzeem, A.K., Chowdhury, M., & Yasmin, N. (2020). Deep convolutional neural networks model-based brain tumor detection in brain MRI images. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 909–914, IEEE. https://doi.org/10.1109/I-SMAC49090.2020.9243461.
  • Tiwari, A., Srivastava, S., & Pant, M. (2020). Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131:244–260. https://doi.org/10.1016/j.patrec.2019.11.020.
  • Wikipedia, (2013). Relief (feature selection), https://en.wikipedia. org/wiki/Relief_(feature_selection), online; accessed 14 December 2022.
  • Xu, Y., Jia, Z., Ai, Y., Zhang, F., Lai, M., Eric, I., & Chang, C. (2015). Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), 947–951, IEEE. https://doi.org/10.1109/ICASSP.2015.7178109.
  • Zacharaki, E.I., Wang, S., Chawla, S., Soo Yoo, D., Wolf, R., Melhem, E.R., & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 62(6):1609–1618. https://doi.org/10.1002/mrm.22147.
There are 23 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Computer Engineering
Authors

İclal Özcan 0000-0001-7414-7425

Serkan Öztürk 0000-0002-0309-3420

Publication Date September 3, 2023
Submission Date May 6, 2023
Published in Issue Year 2023Volume: 26 Issue: 3

Cite

APA Özcan, İ., & Öztürk, S. (2023). BEYİN MR GÖRÜNTÜLERİNDEN TÜMÖR TESPİTİ İÇİN DERİN ÖĞRENMEYE DAYALI HİBRİT MODELLER. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 718-733. https://doi.org/10.17780/ksujes.1293378
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