Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning

Ömer Kasım; Salih Çelik

doi:10.29002/asujse.820599

Research Article

Year 2020, , 187 - 198, 30.12.2020

Ömer Kasım , Salih Çelik

https://doi.org/10.29002/asujse.820599

Cited By: 1

Abstract

References

[1] G.S. Tandel, M. Biswas, O.G. Kakde, A. Tiwari, H.S. Suri, M. Turk, B.K. Madhusudhan, A review on a deep learning perspective in brain cancer classification, Cancers, 11:1 (2019) 111.
[2] H. Mohsen, E.S.A. El-Dahshan, E.S.M. El-Horbaty, A.B.M. Salem, Classification using deep learning neural networks for brain tumors, Future Computing and Informatics Journal, 3:1 (2018) 68-71.
[3] Y. Yang, L.F. Yan, X. Zhang, Y. Han, H.Y. Nan, Y.C. Hu, X.W. Ge, Glioma grading on conventional MR images: a deep learning study with transfer learning, Frontiers in neuroscience, 12 (2018) 804.
[4] T. Kaur, T.K. Gandhi, Deep convolutional neural networks with transfer learning for automated brain image classification, Machine Vision and Applications, 31 (2020) 1-16.
[5] G.B. Praveen, A. Agrawal, Multi stage classification and segmentation of brain tumor, 3rd International Conference on Computing for Sustainable Global Development (2016).
[6] M. Havaei, A. Davy, W.D. Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jadoin, H. Larochelle, Brain tumor segmentation with deep neural networks, Medical Image Analysis, (2016).
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[8] S. Kazdal, B. Doğan, A.Y. Çamurcu, Computer-aided detection of brain tumors using image processing techniques 23nd Signal Processing and Communications Applications Conference, (2015) 863-866.
[9] M. Nazir, F. Wahid, S.A. Khan, A simple and intelligent approach for brain MRI classification, Journal of Intelligent & Fuzzy Systems, 28:3 (2015) 1127-1135.
[10] A.M. Dawud, K. Yurtkan, H. Oztoprak, Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning, Computational Intelligence and Neuroscience, (2019).
[11] N.B. Bahadure, A.K. Ray, H.P. Thethi, Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. Journal of digital imaging, 31:4 (2018) 477-489.
[12] M. Sajjada, S. Khanb, K. Muhammad, W. Wuc, A. Ullah, S.W. Baik, Multi-grade brain tumor classification using deep CNN with extensive data augmentation, Journal of Computational Science, 30 (2019) 174-182.
[13] M.N. Tajik, A. Rehman, W. Khan, B. Khan, Texture feature selection using GA for classification of human brain MRI scans, In International Conference on Swarm Intelligence, (2016) 233-244).
[14] M. Toğaçar, Z. Cömert, B. Ergen, Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method, Expert Systems with Applications, 149 (2020) 113274.
[15] A. Ari, D. Hanbay, Deep learning based brain tumor classification and detection system, Turkish Journal of Electrical Engineering & Computer Sciences, 26:5 (2018) 2275-2286.
[16] Rembrandt The Cancer Imaging Archive, http://doi.org/10.7937/K9/TCIA.2015.588OZUZB
[17] D. Barboriak, Data from RIDER_NEURO_MRI, The Cancer ImagingArchive, (2015). http://doi.org/10.7937/K9/TCIA.2015.VOSN3HN1
[18] K.M. Schmainda, M. Prah, Data from Brain-Tumor-Progression The Cancer Imaging Archive, (2018) http://doi.org/10.7937/K9/TCIA.2018.15quzvnb
[19] K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S.Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The cancer imaging archive (TCIA): Maintaining and operating a public information repository, Journal of digital imaging, 26:6 (2013) 1045–1057.
[20] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Communications of the ACM 60:6 (2017) 84-90.
[21] I. Kononenko, Estimating attributes: analysis and extensions of RELIEF. In European conference on machine learning, (1994) 171-182.
[22] L. Jin, Q. Zeng, J. He, Y. Feng, S. Zhou, Y. Wu, A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease, Behavioural brain research, 356 (2019) 400-407.
[23] P. Kaur, G. Singh, P. Kaur, Classification and Validation of MRI Brain Tumor Using Optimised Machine Learning Approach, International Conference on Data Science, Machine Learning & Applications, (2020) 172-189.
[24] A. Ari, N. Alpaslan, D. Hanbay, Computer-aided tumor detection system using brain MR images, Medical Technologies National Conference, (2015) 1-4.
[25] M. Ghasemi, M. Kelarestaghi, F. Eshghi, A. Sharifi, AFDL: a new adaptive fuzzy dictionary learning for medical image classification, Pattern Analysis and Applications, (2020) 1-20.
[26] J. Amin, M. Sharif, M. Yasmin, S.L. Fernandes, A distinctive approach in brain tumor detection and classification using MRI, Pattern Recognition Letters , (2017)
[27] B. Doğan, S.K. Çalık, Ö. Demir, Beyin Tümörlerinin Biçimsel Yapılandırma Kullanılarak Bilgisayar Destekli Tespiti, Uludağ University Journal of The Faculty of Engineering, 21:2 (2016) 257-268.

Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning

Year 2020, , 187 - 198, 30.12.2020

Ömer Kasım , Salih Çelik

https://doi.org/10.29002/asujse.820599

Cited By: 1

Abstract

This study includes investigating the presence of tumor regions in Magnetic Resonance Imaging (MRI) slices. Since the MRI taken from a patient consists of many slices, it may take time for experts to review these images. The aim of the study is to evaluate the specialist's MRI slices more quickly. The image of each MRI slice taken from the patient was applied to the Alexnet transfer learning algorithm and the properties of the image were obtained. These features are optimized with the Relieff feature selection algorithm to achieve optimum success. The highest accuracy has been achieved with the support vector machine classifier, in which optimized features are used. The study was validated with 3 different combinations by training with two datasets and testing with the other. Thus, a method that can work under different conditions were obtained. The performance metrics of the study were obtained by taking the average of the successes obtained from each data set. MRIs were trained with Alexnet transfer learning model and performance analysis was performed on the obtained classification models. The feature optimization used both increased the success to 97.55% and reduced the processing time from 0.4064 to 0.3045 seconds. The proposed model with a high success rate and a rapid classification is expected to assist the expert in both diagnosis and treatment planning.

Keywords

Brain Magnetic Resonance Imaging, Feature Extraction with Alexnet Transfer Learning, Relieff Feature Selection, Support Vector Machines

References

[1] G.S. Tandel, M. Biswas, O.G. Kakde, A. Tiwari, H.S. Suri, M. Turk, B.K. Madhusudhan, A review on a deep learning perspective in brain cancer classification, Cancers, 11:1 (2019) 111.
[2] H. Mohsen, E.S.A. El-Dahshan, E.S.M. El-Horbaty, A.B.M. Salem, Classification using deep learning neural networks for brain tumors, Future Computing and Informatics Journal, 3:1 (2018) 68-71.
[3] Y. Yang, L.F. Yan, X. Zhang, Y. Han, H.Y. Nan, Y.C. Hu, X.W. Ge, Glioma grading on conventional MR images: a deep learning study with transfer learning, Frontiers in neuroscience, 12 (2018) 804.
[4] T. Kaur, T.K. Gandhi, Deep convolutional neural networks with transfer learning for automated brain image classification, Machine Vision and Applications, 31 (2020) 1-16.
[5] G.B. Praveen, A. Agrawal, Multi stage classification and segmentation of brain tumor, 3rd International Conference on Computing for Sustainable Global Development (2016).
[6] M. Havaei, A. Davy, W.D. Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jadoin, H. Larochelle, Brain tumor segmentation with deep neural networks, Medical Image Analysis, (2016).
[7] K. Dimililer, A. Ilhan, Effect of Image Enhancement on MRI Brain Images with Neural Networks, Procedia Computer Science, 102 (2016) 39–44.
[8] S. Kazdal, B. Doğan, A.Y. Çamurcu, Computer-aided detection of brain tumors using image processing techniques 23nd Signal Processing and Communications Applications Conference, (2015) 863-866.
[9] M. Nazir, F. Wahid, S.A. Khan, A simple and intelligent approach for brain MRI classification, Journal of Intelligent & Fuzzy Systems, 28:3 (2015) 1127-1135.
[10] A.M. Dawud, K. Yurtkan, H. Oztoprak, Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning, Computational Intelligence and Neuroscience, (2019).
[11] N.B. Bahadure, A.K. Ray, H.P. Thethi, Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. Journal of digital imaging, 31:4 (2018) 477-489.
[12] M. Sajjada, S. Khanb, K. Muhammad, W. Wuc, A. Ullah, S.W. Baik, Multi-grade brain tumor classification using deep CNN with extensive data augmentation, Journal of Computational Science, 30 (2019) 174-182.
[13] M.N. Tajik, A. Rehman, W. Khan, B. Khan, Texture feature selection using GA for classification of human brain MRI scans, In International Conference on Swarm Intelligence, (2016) 233-244).
[14] M. Toğaçar, Z. Cömert, B. Ergen, Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method, Expert Systems with Applications, 149 (2020) 113274.
[15] A. Ari, D. Hanbay, Deep learning based brain tumor classification and detection system, Turkish Journal of Electrical Engineering & Computer Sciences, 26:5 (2018) 2275-2286.
[16] Rembrandt The Cancer Imaging Archive, http://doi.org/10.7937/K9/TCIA.2015.588OZUZB
[17] D. Barboriak, Data from RIDER_NEURO_MRI, The Cancer ImagingArchive, (2015). http://doi.org/10.7937/K9/TCIA.2015.VOSN3HN1
[18] K.M. Schmainda, M. Prah, Data from Brain-Tumor-Progression The Cancer Imaging Archive, (2018) http://doi.org/10.7937/K9/TCIA.2018.15quzvnb
[19] K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S.Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The cancer imaging archive (TCIA): Maintaining and operating a public information repository, Journal of digital imaging, 26:6 (2013) 1045–1057.
[20] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Communications of the ACM 60:6 (2017) 84-90.
[21] I. Kononenko, Estimating attributes: analysis and extensions of RELIEF. In European conference on machine learning, (1994) 171-182.
[22] L. Jin, Q. Zeng, J. He, Y. Feng, S. Zhou, Y. Wu, A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease, Behavioural brain research, 356 (2019) 400-407.
[23] P. Kaur, G. Singh, P. Kaur, Classification and Validation of MRI Brain Tumor Using Optimised Machine Learning Approach, International Conference on Data Science, Machine Learning & Applications, (2020) 172-189.
[24] A. Ari, N. Alpaslan, D. Hanbay, Computer-aided tumor detection system using brain MR images, Medical Technologies National Conference, (2015) 1-4.
[25] M. Ghasemi, M. Kelarestaghi, F. Eshghi, A. Sharifi, AFDL: a new adaptive fuzzy dictionary learning for medical image classification, Pattern Analysis and Applications, (2020) 1-20.
[26] J. Amin, M. Sharif, M. Yasmin, S.L. Fernandes, A distinctive approach in brain tumor detection and classification using MRI, Pattern Recognition Letters , (2017)
[27] B. Doğan, S.K. Çalık, Ö. Demir, Beyin Tümörlerinin Biçimsel Yapılandırma Kullanılarak Bilgisayar Destekli Tespiti, Uludağ University Journal of The Faculty of Engineering, 21:2 (2016) 257-268.

There are 27 citations in total.

Details

Primary Language	English
Subjects	Engineering
Journal Section	Research Article
Authors	Ömer Kasım 0000-0003-4021-5412 Salih Çelik
Publication Date	December 30, 2020
Submission Date	November 3, 2020
Acceptance Date	December 29, 2020
Published in Issue	Year 2020

Cite

APA	Kasım, Ö., & Çelik, S. (2020). Detection of Tumor Slice in Brain Magnetic Resonance Images by Feature Optimized Transfer Learning. Aksaray University Journal of Science and Engineering, 4(2), 187-198. https://doi.org/10.29002/asujse.820599