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MRI görüntüleri üzerinde Derin CNN'ler ve Topluluk Algoritmalarını Kullanarak Beyin Tümörü Tespiti

Yıl 2024, Cilt: 9 Sayı: Issue: 2, 142 - 150
https://doi.org/10.53070/bbd.1455902

Öz

Beyin tümörleri insan ölümünün en yaygın nedenlerinden biridir. Beyin tümörlerinin erken ve doğru tanısı etkili tedavi için çok önemlidir. Sağlık alanında hastalıkların erken teşhisi ve uzman yoğunluğunun azaltılması, teşhiste yapılabilecek hataların en aza indirilmesi amacıyla farklı öğrenme teknikleri kullanılmaktadır. Son yıllarda makine öğrenmesi ve derin öğrenme modellerinin gelişmesiyle birlikte beyin araştırmalarında görüntü işleme çalışmalarında başarılı sonuçlar alınmaya başlanmıştır. Bu çalışmada, MRI görüntülerinden özellik çıkarımı için önceden eğitilmiş derin evrişim sinir ağı yöntemleri tercih edilmiş ve çıkarılan özelliklerden tümörün tespit edilmesi için topluluk öğrenmesi gerçekleştirilmiştir. Analiz sonuçları, beyin tümörlerini tespit etmek için önceden eğitilmiş derin ağlara sahip topluluk tabanlı sınıflandırıcı kullanılarak %100 doğruluk başarısına ulaşıldığını göstermektedir

Kaynakça

  • Bauer, E., and Kohavi, R., 1998. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, 1-38.
  • Bishop, C., 2010. Neural Networks for Pattern Recognition, Oxford University Press.
  • Bishop, C.M., 1995. Neural Network for Pattern Recognition, Microsoft Research Cambridge.
  • Bishop, C.M., 2006. Pattern Recognition and Machine Learning, Springer.
  • Breiman, L., 1996. Bagging Predictors, Vol. 24, Kluwer Academic Publishers.
  • Dong, Y., Zhang, H., Wang, C., and Wang, Y., 2019. Fine-Grained Ship Classification based on Deep Residual Learning for High-Resolution SAR Images, Remote Sens. Lett., 10 (11), 1095-1104.
  • Efron, Bradley., & Tibshirani, Robert. (1994). An introduction to the bootstrap. Chapman & Hall.
  • Gao, H., Zhuang, L., Laurens, van der M., and Kilian Q.W., 2018. Densely Connected Convolutional Networks, Computer Science > Computer Vision and Pattern Recognition, arXiv:1608.06993v5 , https://doi.org/10.48550/arXiv.1608.06993
  • He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778
  • Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q., 2018. Densely Connected Convolutional Networks, Computer Science > Computer Vision and Pattern Recognition, arXiv:1608.06993v5.
  • Kidwell, C.S., and Hsia, A.W., 2006. Imaging of the Brain and Cerebral Vasculature in Patients with Suspected Stroke: Advantages and Disadvantages of CT and MRI, Current neurology and neuroscience reports, 6(1), 9-16.
  • LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., 1998. Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278-2324.
  • Mohsen, H., El-Dahshan, E.S.A, El-Horbaty, E.S.M, and Salem, A.B.M., 2018. Classification using Deep Learning Neural Networks for Brain Tumors, Future Computing and Informatics Journal, 3(1), 68-71.
  • Nayak, D.R., Padhy, N., Kumar Mallick, P., and Singh, A., 2022. A Deep Autoencoder Approach for Detection of Brain Tumor Images, Computers, and Electrical Engineering, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2022.108238.
  • Nickparvar, M., 2021. Brain Tumor MRI Dataset, doi:10.34740/KAGGLE/DSV/2645886.
  • Opitz, D., and Maclin, R., 1999. Popular Ensemble Methods: An Empirical Study. In Journal of Artificial Intelligence Research, Vol. 11.
  • Ozer, E., 2023. Early Diagnosis of Epileptic Seizures over EEG Signals using Deep Learning Approach, Mimar Sinan Fine Arts University, Institute of Science and Technology, PhD Thesis.
  • Patterson, J., and Gibson, A., 2017. Deep Learning A Practitioner’s Approach, 1st Edition, O’Reilly.
  • Pendela, K., Revathi, K.G.M., Belsam J.A., 2023. Optimization-Enabled Hybrid Deep Learning for Brain Tumor Detection and Classification from MRI, Biomedical Signal Processing and Control, 84, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.104955.
  • Polikar, R., 2012. Ensemble learning. Ensemble Machine Learning, 10th ed. Boston, Springer, 1-34.
  • Qin, C., Li, B., and Han, B., 2023. Fast Brain Tumor Detection using Adaptive Stochastic Gradient Descent on Shared-Memory Parallel Environment, Engineering Applications of Artificial Intelligence, 120, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2022.105816.
  • Rammurthy, D., and Mahesh, P.K., 2022. Whale Harris Hawks Optimization based Deep Learning Classifier for Brain Tumor Detection using MRI images, Journal of King Saud University - Computer and Information Sciences, 34(6), 3259-3272, https://doi.org/10.1016/j.jksuci.2020.08.006.
  • Shahzadi, I., Tang, T.B., Meriadeau, F., and Quyyum, A., 2018. CNN-LSTM: Cascaded framework for brain tumour classification, IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES, Malaysia, 633-637.
  • Swati, Z.N.K, Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., and Lu, J., 2019. Brain Tumor Classification for MR Images using Transfer Learning and Fine-Tuning, Computerized Medical Imaging and Graphics, 75, 34-46.
  • Vani, N., Sowmya, A., and Jayamma, N., 2017. Brain Tumor Classification using Support Vector Machine, International Research Journal of Engineering and Technology (IRJET), 4(7), 792-796.
  • Wei, D., Anurag, B., and Jianing, W., 2018. Deep Learning Essentials: Your Hands-on Guide to the Fundamentals of Deep Learning and Neural Network Modeling, Packt Publishing.
  • WHO, World Health Organization Report, 2021. Link: https://www.who.int/health-topics/cancer
  • Yasrab, R., Gu, N., and Zhang, X., 2017. An encoder-decoder based Convolution Neural Network (CNN) for future Advanced Driver Assistance System (ADAS), Applied Sciences (Switzerland), 7(4). https://doi.org/10.3390/app7040312

Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images

Yıl 2024, Cilt: 9 Sayı: Issue: 2, 142 - 150
https://doi.org/10.53070/bbd.1455902

Öz

Brain tumors are one of the most common causes of human death. Early and accurate diagnosis of brain tumors is very important for effective treatment. Different learning techniques have been used in the field of health to diagnose diseases early and reduce the intensity of experts, as well as to minimize errors that may be made in diagnosis. In recent years, successful results have begun to be obtained in image processing studies in brain research, with the development of machine learning and deep learning models. In this study, pretrained deep convolution neural network methods are preferred to feature extraction from MRI images, and ensemble learning is performed to detect the tumor from extracted features. Analysis results show a 100% accuracy score, using the ensemble-based classifier with the pretrained deep networks to detect brain tumors.

Kaynakça

  • Bauer, E., and Kohavi, R., 1998. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, 1-38.
  • Bishop, C., 2010. Neural Networks for Pattern Recognition, Oxford University Press.
  • Bishop, C.M., 1995. Neural Network for Pattern Recognition, Microsoft Research Cambridge.
  • Bishop, C.M., 2006. Pattern Recognition and Machine Learning, Springer.
  • Breiman, L., 1996. Bagging Predictors, Vol. 24, Kluwer Academic Publishers.
  • Dong, Y., Zhang, H., Wang, C., and Wang, Y., 2019. Fine-Grained Ship Classification based on Deep Residual Learning for High-Resolution SAR Images, Remote Sens. Lett., 10 (11), 1095-1104.
  • Efron, Bradley., & Tibshirani, Robert. (1994). An introduction to the bootstrap. Chapman & Hall.
  • Gao, H., Zhuang, L., Laurens, van der M., and Kilian Q.W., 2018. Densely Connected Convolutional Networks, Computer Science > Computer Vision and Pattern Recognition, arXiv:1608.06993v5 , https://doi.org/10.48550/arXiv.1608.06993
  • He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778
  • Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q., 2018. Densely Connected Convolutional Networks, Computer Science > Computer Vision and Pattern Recognition, arXiv:1608.06993v5.
  • Kidwell, C.S., and Hsia, A.W., 2006. Imaging of the Brain and Cerebral Vasculature in Patients with Suspected Stroke: Advantages and Disadvantages of CT and MRI, Current neurology and neuroscience reports, 6(1), 9-16.
  • LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P., 1998. Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278-2324.
  • Mohsen, H., El-Dahshan, E.S.A, El-Horbaty, E.S.M, and Salem, A.B.M., 2018. Classification using Deep Learning Neural Networks for Brain Tumors, Future Computing and Informatics Journal, 3(1), 68-71.
  • Nayak, D.R., Padhy, N., Kumar Mallick, P., and Singh, A., 2022. A Deep Autoencoder Approach for Detection of Brain Tumor Images, Computers, and Electrical Engineering, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2022.108238.
  • Nickparvar, M., 2021. Brain Tumor MRI Dataset, doi:10.34740/KAGGLE/DSV/2645886.
  • Opitz, D., and Maclin, R., 1999. Popular Ensemble Methods: An Empirical Study. In Journal of Artificial Intelligence Research, Vol. 11.
  • Ozer, E., 2023. Early Diagnosis of Epileptic Seizures over EEG Signals using Deep Learning Approach, Mimar Sinan Fine Arts University, Institute of Science and Technology, PhD Thesis.
  • Patterson, J., and Gibson, A., 2017. Deep Learning A Practitioner’s Approach, 1st Edition, O’Reilly.
  • Pendela, K., Revathi, K.G.M., Belsam J.A., 2023. Optimization-Enabled Hybrid Deep Learning for Brain Tumor Detection and Classification from MRI, Biomedical Signal Processing and Control, 84, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.104955.
  • Polikar, R., 2012. Ensemble learning. Ensemble Machine Learning, 10th ed. Boston, Springer, 1-34.
  • Qin, C., Li, B., and Han, B., 2023. Fast Brain Tumor Detection using Adaptive Stochastic Gradient Descent on Shared-Memory Parallel Environment, Engineering Applications of Artificial Intelligence, 120, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2022.105816.
  • Rammurthy, D., and Mahesh, P.K., 2022. Whale Harris Hawks Optimization based Deep Learning Classifier for Brain Tumor Detection using MRI images, Journal of King Saud University - Computer and Information Sciences, 34(6), 3259-3272, https://doi.org/10.1016/j.jksuci.2020.08.006.
  • Shahzadi, I., Tang, T.B., Meriadeau, F., and Quyyum, A., 2018. CNN-LSTM: Cascaded framework for brain tumour classification, IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES, Malaysia, 633-637.
  • Swati, Z.N.K, Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., and Lu, J., 2019. Brain Tumor Classification for MR Images using Transfer Learning and Fine-Tuning, Computerized Medical Imaging and Graphics, 75, 34-46.
  • Vani, N., Sowmya, A., and Jayamma, N., 2017. Brain Tumor Classification using Support Vector Machine, International Research Journal of Engineering and Technology (IRJET), 4(7), 792-796.
  • Wei, D., Anurag, B., and Jianing, W., 2018. Deep Learning Essentials: Your Hands-on Guide to the Fundamentals of Deep Learning and Neural Network Modeling, Packt Publishing.
  • WHO, World Health Organization Report, 2021. Link: https://www.who.int/health-topics/cancer
  • Yasrab, R., Gu, N., and Zhang, X., 2017. An encoder-decoder based Convolution Neural Network (CNN) for future Advanced Driver Assistance System (ADAS), Applied Sciences (Switzerland), 7(4). https://doi.org/10.3390/app7040312
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme, Makine Öğrenme (Diğer)
Bölüm PAPERS
Yazarlar

Ezgi Özer 0000-0003-1567-2216

Erken Görünüm Tarihi 24 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 20 Mart 2024
Kabul Tarihi 6 Ağustos 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: Issue: 2

Kaynak Göster

APA Özer, E. (2024). Brain Tumor Detection using Deep CNNs and Ensemble Algorithms over MRI Images. Computer Science, 9(Issue: 2), 142-150. https://doi.org/10.53070/bbd.1455902

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