Bayes optimizasyon tabanlı SVM sınıflandırıcı ve ince-ayar tabanlı derin özelliklerinin kombinasyonu kullanılarak Beyin Tümörü Tespiti
Öz
En sık görülen kanser türlerinden biri olan beyin tümörü ölümcül bir hastalıktır. Bu nedenle bu hastalığın doğru teşhisi ve tümörün tipinin belirlenmesi erken tedavi açısından büyük önem taşımaktadır. Bu bağlamda, son zamanlarda beyin tümörü sınıflandırmasında yaşanan problemler için derin öğrenmeye dayalı otomatik sistemlerin geliştirilmesine yönelik araştırmalar ve ilgi artmıştır. Bu çalışmada, beyin tümörlerinin sınıflandırılması için Bayesian optimizasyon tabanlı Destek Vektör Makinesi (DVM) sınıflandırıcısı ve Evrişimsel Sinir Ağı (ESA) tabanlı derin öznitelikler topluluğuna dayalı benzersiz bir tasarım önerilmiştir. Bu modelde öncelikle beyin MRI görüntüleri iyileştirildi. İkinci olarak, derin öznitelikler, önceden eğitilmiş ESA tabanlı derin mimariler kullanılarak çıkartıldı ve ardından birleştirildi. Daha sonra, MrMr algoritması ile bu derin özelliklerden etkili ve ayırt edici özellikler seçildi. Son olarak, bu özellikler, Bayes optimizasyon algoritmasına dayalı DVM sınıflandırıcısının eğitiminde kullanıldı. Önerilen sistemi test etmek için, meningiom, glioma ve hipofiz gibi beyin tümörü görüntülerini içeren Figshare adlı bir veri seti kullanıldı. Deneysel çalışmalarda, önerilen modelin doğruluk skoru diğer çalışmalardan daha başarılı olduğu gözlemlenmiştir.
Anahtar Kelimeler
Beyin Tümör Sınıflandırımlası Özellik Seçimi Evrişimsel Sinir Ağları Destek Vektör Makinesi Bayes Optimizasyon
Kaynakça
- Abir, T.A., Siraji, J.A., Ahmed, E., & Khulna, B. (2018). Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol., 4(8), 65–79.
- Afshar, P., Mohammadi, A., & Plataniotis, K.N. (2018). Brain tumor type classification via capsule networks. arXiv preprint: arXiv:1802.10200.
- Afshar, P., Plataniotis, K.N., & Mohammadi, A. (2019). Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1368-1372.
- Amin, J., Sharif, M., Raza, M., Saba, T., & Rehman, A. (2019). Brain Tumor Classification: Feature Fusion. In 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1-6.
- Amin J., Sharif, M., Gul, N., Yasmin, M., & Shad, S.A. (2020). Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognition Letters, 129, 115-122.
- Ari, A. (2019). Detection and classification of brain tumors from MR images based on deep learning. PhD. Thesis, Inonu University, Malatya, Turkey.
- Ari, A., Alcin, O.F., & Hanbay, D. (2020). Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines. Biomedical Journal of Scientific & Technical Research, 25(3), 1937-1944.
- Ayadi, W., Charfi, I., Elhamzi, W., & Atri, M. (2020). Brain tumor classification based on hybrid approach. The Visual Computer, 1-11.
- Bodapati, J.D., Shaik, N.S., Naralasetti, V., & Mundukur, N.B. (2020). Joint training of two-channel deep neural network for brain tumor classification. Signal, Image and Video Processing, 1-8.
- Cheng, J., et al. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one, 10(10), e0140381.
- Cheng, J., et al. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE, 11(6), e0157112.
- Cheng, J. (2018). Brain tumor dataset (Figshare dataset), https://doi.org/10.6084/m9.figshare.1512427.v5. Accessed 30 May 2018.
- Cortes, C., & Vapnik, V. (1995). Support-vector networks, Machine Learning, 20(3), 273.
- Deepak, S., & Ameer, P.M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in biology and medicine, 111, 103345.
- Deepak, S., & Ameer, P.M. (2020). Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 1-13.
- Demir, F., Turkoglu, M., Aslan, M., & Sengur, A. (2020). A new pyramidal concatenated CNN approach for environmental sound classification. Applied Acoustics, 170, 107520.
- Gulgezen, G., Cataltepe, Z., & Yu, L. (2009). Stable feature selection using MRMR algorithm. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 596-599.
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K.Q. (2018). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708.
- Kaur, T., & Gandhi, T.K. (2020). Deep convolutional neural networks with transfer learning for automated brain image classification. Machine Vision and Applications, 31, 1-16.
- Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. InAdvances in neural information processing systems, 1097-1105.
- Pashaei, A., Sajedi, H., & Jazayeri, N. (2019). Brain tumor classification via convolutional neural network and extreme learning machines. In 2018 8th International conference on computer and knowledge engineering (ICCKE), pp. 314-319.
- Pelikan, M., Goldberg, D., & Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the genetic and evolutionary computation conference GECCO-99, 1, 525-532.
- Rehman, A., Naz, S., Razzak, M.I., Akram, F., & Imran, M. (2020). A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing, 39(2), 757-775.
- Swati, Z.N.K. et al. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 75, 34-46.
- Toğaçar, M., Ergen, B., & Cömert, Z. (2020a) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23-39.
- Toğaçar, M., Ergen, B., & Cömert, Z. (2020b). Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement, 153, 107459.
- Turkoglu, M., & Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering & Computer Sciences, 27(3), 1636-1651.
- Turkoglu, M. (2020). COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Applied Intelligence, 1-14.
- Yaslan, Y., & Cataltepe, Z. (2009). Audio genre classification with co-mrmr. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 408-411.
Brain Tumor Detection using a combination of Bayesian optimization based SVM classifier and fine-tuned based deep features
Öz
Brain tumor, one of the most common types of cancer, is a fatal disease. Therefore, accurate diagnosis of this disease and determining the type of tumor are of great importance in terms of early treatment. In this context, research, and interest in the development of automatic systems for the problems experienced in brain tumor classification, based on deep learning, have increased recently. In this study, a unique framework is proposed, which is based on Bayesian optimization-based Support Vector Machine (SVM) classifier and Convolutional Neural Network (CNN) based deep features ensemble, for the classification of brain tumors. In this model, brain MRI images are first improved. Second, the deep features are extracted using pre-trained CNN-based deep architectures and then combined. Later, effective, and distinctive features are selected from these deep features with the MrMr algorithm. Finally, these features are used in the training of the SVM classifier based on the Bayesian optimization algorithm. A dataset named Figshare, containing brain tumor images such as meningioma, glioma, and pituitary, is used to test the proposed system. In the experimental studies, the accuracy score of the model proposed was observed to be more successful than that of the other studies.
Anahtar Kelimeler
Brain Tumor Classification Feature selection Convolutional Neural Network Support Vector Machine Bayesian optimization
Kaynakça
- Abir, T.A., Siraji, J.A., Ahmed, E., & Khulna, B. (2018). Analysis of a novel MRI based brain tumour classification using probabilistic neural network (PNN). Int. J. Sci. Res. Sci. Eng. Technol., 4(8), 65–79.
- Afshar, P., Mohammadi, A., & Plataniotis, K.N. (2018). Brain tumor type classification via capsule networks. arXiv preprint: arXiv:1802.10200.
- Afshar, P., Plataniotis, K.N., & Mohammadi, A. (2019). Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1368-1372.
- Amin, J., Sharif, M., Raza, M., Saba, T., & Rehman, A. (2019). Brain Tumor Classification: Feature Fusion. In 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1-6.
- Amin J., Sharif, M., Gul, N., Yasmin, M., & Shad, S.A. (2020). Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognition Letters, 129, 115-122.
- Ari, A. (2019). Detection and classification of brain tumors from MR images based on deep learning. PhD. Thesis, Inonu University, Malatya, Turkey.
- Ari, A., Alcin, O.F., & Hanbay, D. (2020). Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines. Biomedical Journal of Scientific & Technical Research, 25(3), 1937-1944.
- Ayadi, W., Charfi, I., Elhamzi, W., & Atri, M. (2020). Brain tumor classification based on hybrid approach. The Visual Computer, 1-11.
- Bodapati, J.D., Shaik, N.S., Naralasetti, V., & Mundukur, N.B. (2020). Joint training of two-channel deep neural network for brain tumor classification. Signal, Image and Video Processing, 1-8.
- Cheng, J., et al. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one, 10(10), e0140381.
- Cheng, J., et al. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE, 11(6), e0157112.
- Cheng, J. (2018). Brain tumor dataset (Figshare dataset), https://doi.org/10.6084/m9.figshare.1512427.v5. Accessed 30 May 2018.
- Cortes, C., & Vapnik, V. (1995). Support-vector networks, Machine Learning, 20(3), 273.
- Deepak, S., & Ameer, P.M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in biology and medicine, 111, 103345.
- Deepak, S., & Ameer, P.M. (2020). Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 1-13.
- Demir, F., Turkoglu, M., Aslan, M., & Sengur, A. (2020). A new pyramidal concatenated CNN approach for environmental sound classification. Applied Acoustics, 170, 107520.
- Gulgezen, G., Cataltepe, Z., & Yu, L. (2009). Stable feature selection using MRMR algorithm. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 596-599.
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K.Q. (2018). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708.
- Kaur, T., & Gandhi, T.K. (2020). Deep convolutional neural networks with transfer learning for automated brain image classification. Machine Vision and Applications, 31, 1-16.
- Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. InAdvances in neural information processing systems, 1097-1105.
- Pashaei, A., Sajedi, H., & Jazayeri, N. (2019). Brain tumor classification via convolutional neural network and extreme learning machines. In 2018 8th International conference on computer and knowledge engineering (ICCKE), pp. 314-319.
- Pelikan, M., Goldberg, D., & Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the genetic and evolutionary computation conference GECCO-99, 1, 525-532.
- Rehman, A., Naz, S., Razzak, M.I., Akram, F., & Imran, M. (2020). A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing, 39(2), 757-775.
- Swati, Z.N.K. et al. (2019). Brain tumor classification for MR images using transfer learning and fine-tuning. Computerized Medical Imaging and Graphics, 75, 34-46.
- Toğaçar, M., Ergen, B., & Cömert, Z. (2020a) Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23-39.
- Toğaçar, M., Ergen, B., & Cömert, Z. (2020b). Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement, 153, 107459.
- Turkoglu, M., & Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering & Computer Sciences, 27(3), 1636-1651.
- Turkoglu, M. (2020). COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble. Applied Intelligence, 1-14.
- Yaslan, Y., & Cataltepe, Z. (2009). Audio genre classification with co-mrmr. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 408-411.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 29 Temmuz 2021 |
| Yayımlanma Tarihi | 30 Kasım 2021 |
| Yayımlandığı Sayı | Yıl 2021 Sayı: 27 |
