MULTIPLE CLASSIFICATION OF BRAIN TUMORS FOR EARLY DETECTION USING A NOVEL CONVOLUTIONAL NEURAL NETWORK MODEL

Year 2023, Volume: 31 Issue: 1, 491 - 500, 29.04.2023

Muhammed Çelik , Özkan İnik

https://doi.org/10.31796/ogummf.1158526

Abstract

Brain tumors can have very dangerous and fatal effects if not diagnosed early. These are diagnosed by specialized doctors using biopsy samples taken from the brain. This process is exhausting and wastes doctors' time too much. Researchers have been working to develop a quick and accurate way for identifying and classifying brain tumors in order to overcome these drawbacks. Computer-assisted technologies are utilized to support doctors and specialists in making more efficient and accurate decisions. Deep learning-based methods are one of these technologies and have been used extensively in recent years. However, there is still a need to explore architectures with higher accuracy performance. For this purpose, in this paper proposed a novel convolutional neural network (CNN) which has twenty-four layers to multi-classify brain tumors from brain MRI images for early diagnosis. In order to demonstrate the effectiveness of the proposed model, various comparisons and tests were carried out. Three different state-of-the-art CNN models were used in the comparison: AlexNet, ShuffleNet and SqueezeNet. At the end of the training, proposed model is achieved highest accuracy of 92.82% and lowest loss of 0.2481. In addition, ShuflleNet determines the second highest accuracy at 90.17%. AlexNet has the lowest accuracy at 80.5% with 0.4679 of loss. These results demonstrate that the proposed CNN model provides greater precision and accuracy than the state-of-art CNN models.

Keywords

Deep learning, CNN models, pre-trained models, brain MRI images, classification

YENİ BİR EVRİŞİMLİ SİNİR AĞI MODELİ KULLANILARAK ERKEN TEŞHİS İÇİN BEYİN TÜMÖRLERİNİN ÇOKLU SINIFLANDIRMASI

Abstract

Beyin tümörleri erken teşhis edilmezse çok tehlikeli ve ölümcül etkilere sahip olabilir. Beyin tümörleri, uzman doktorlar tarafından beyinden alınan biyopsi örnekleri kullanılarak teşhis edilir. Bu süreç yorucudur ve doktorların çok fazla zamanını harcar. Araştırmacılar, bu dezavantajların üstesinden gelmek amacıyla beyin tümörlerini tanımlamak ve sınıflandırmak için hızlı ve doğru bir yol geliştirmeye çalışmaktadırlar. Doktorların ve uzmanların daha verimli ve doğru kararlar vermelerini desteklemek için bilgisayar destekli teknolojiler kullanılmaktadır. Derin öğrenme tabanlı yöntemler de bu teknolojilerden biridir ve son yıllarda yoğun olarak kullanılmaya başlanmıştır. Bununla birlikte, daha yüksek doğruluk performansına sahip mimarileri keşfetmeye hala ihtiyaç vardır. Bu amaçla, bu çalışmada erken teşhis için beyin MR görüntülerinden beyin tümörlerini çoklu sınıflandırmak için yirmi dört katmana sahip yeni bir evrişimli sinir ağı (ESA) önerilmiştir. Önerilen modelin etkinliğini göstermek için çeşitli karşılaştırmalar ve testler yapılmıştır. Karşılaştırmada üç farklı son teknoloji CNN modeli kullanılmıştır: AlexNet, ShuffleNet ve SqueezeNet. Eğitim sonunda önerilen model %92,82 ile en yüksek doğruluk ve 0,2481 ile en düşük kayıp elde edilmiştir. Ek olarak, ShuflleNet %90,17 ile ikinci en yüksek doğruluk değerine ulaşmıştır. AlexNet, 0,4679 kayıpla %80,5 ile en düşük doğruluğa sahiptir. Bu Sonuçlar, önerilen CNN modelinin, son teknoloji CNN modellerinden daha fazla kesinlik ve doğruluk sağladığını göstermektedir.

Keywords

Derin öğrenme, ESA modelleri, önceden eğitilmiş modeller, Beyin MR görüntüleri, sınıflandırma

References

• J. Mao et al., “Pseudo-labeling generative adversarial networks for medical image classification,” Computers in Biology and Medicine, vol. 147, p. 105729, Aug. 2022, doi: 10.1016/J.COMPBIOMED.2022.105729.
• B. Fu, M. Zhang, J. He, Y. Cao, Y. Guo, and R. Wang, “StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images,” Computer Methods and Programs in Biomedicine, vol. 221, p. 106924, Jun. 2022, doi: 10.1016/J.CMPB.2022.106924.
• W. Zhou, H. Wang, and Z. Wan, “Ore Image Classification Based on Improved CNN,” Computers and Electrical Engineering, vol. 99, p. 107819, Apr. 2022, doi: 10.1016/J.COMPELECENG.2022.107819.
• K. Uyar and E. Ülker, “Gender Classification with A Novel Convolutional Neural Network (CNN) Model and Comparison with other Machine Learning and Deep Learning CNN Models.” [Online]. Available: https://www.researchgate.net/publication/330279739.
• Z. Li, M. Dong, S. Wen, X. Hu, P. Zhou, and Z. Zeng, “CLU-CNNs: Object detection for medical images,” Neurocomputing, vol. 350, pp. 53–59, Jul. 2019, doi: 10.1016/J.NEUCOM.2019.04.028.
• C. B. Gonçalves, J. R. Souza, and H. Fernandes, “CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images,” Comput Biol Med, vol. 142, Mar. 2022, doi: 10.1016/J.COMPBIOMED.2021.105205.
• Ö. İnik, A. Ceyhan, E. Balcıoğlu, and E. Ülker, “A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network,” Computers in Biology and Medicine, vol. 112, p. 103350, Sep. 2019, doi: 10.1016/J.COMPBIOMED.2019.103350.
• D. Zhao, Y. Liu, H. Yin, and Z. Wang, “A novel multi-scale CNNs for false positive reduction in pulmonary nodule detection,” Expert Systems with Applications, vol. 207, p. 117652, Nov. 2022, doi: 10.1016/J.ESWA.2022.117652.
• M. Fradi, E. hadi Zahzah, and M. Machhout, “Real-time application based CNN architecture for automatic USCT bone image segmentation,” Biomedical Signal Processing and Control, vol. 71, p. 103123, Jan. 2022, doi: 10.1016/J.BSPC.2021.103123.
• L. Kang, Z. Zhou, J. Huang, and W. Han, “Renal tumors segmentation in abdomen CT Images using 3D-CNN and ConvLSTM,” Biomedical Signal Processing and Control, vol. 72, p. 103334, Feb. 2022, doi: 10.1016/J.BSPC.2021.103334.
• R. Karthik, R. Menaka, H. M, and D. Won, “Contour-enhanced attention CNN for CT-based COVID-19 segmentation,” Pattern Recognition, vol. 125, p. 108538, May 2022, doi: 10.1016/J.PATCOG.2022.108538.
• Ö. Inik and E. Ülker, “Optimization of deep learning based segmentation method,” Soft Computing, vol. 26, no. 7, pp. 3329–3344, Apr. 2022, doi: 10.1007/S00500-021-06711-3/TABLES/9.
• S. Niyas, S. J. Pawan, M. Anand Kumar, and J. Rajan, “Medical image segmentation with 3D convolutional neural networks: A survey,” Neurocomputing, vol. 493, pp. 397–413, Jul. 2022, doi: 10.1016/J.NEUCOM.2022.04.065.
• B. Srikanth and S. Venkata Suryanarayana, “Multi-Class classification of brain tumor images using data augmentation with deep neural network,” Materials Today: Proceedings, Mar. 2021, doi: 10.1016/J.MATPR.2021.01.601.
• K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Sep. 2014, doi: 10.48550/arxiv.1409.1556.
• S. Deepak and P. M. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Computers in Biology and Medicine, vol. 111, p. 103345, Aug. 2019, doi: 10.1016/J.COMPBIOMED.2019.103345.
• A. Krizhevsky and G. Inc, “One weird trick for parallelizing convolutional neural networks,” Apr. 2014, doi: 10.48550/arxiv.1404.5997.
• Z. Jia and D. Chen, “Brain Tumor Identification and Classification of MRI images using deep learning techniques,” IEEE Access, pp. 1–1, Aug. 2020, doi: 10.1109/ACCESS.2020.3016319.
• E. Irmak, “Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework,” Iranian Journal of Science and Technology - Transactions of Electrical Engineering, vol. 45, no. 3, pp. 1015–1036, Sep. 2021, doi: 10.1007/S40998-021-00426-9/TABLES/11.
• H. Siar and M. Teshnehlab, “Diagnosing and Classification Tumors and MS Simultaneous of Magnetic Resonance Images Using Convolution Neural Network∗,” 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2019, Apr. 2019, doi: 10.1109/CFIS.2019.8692148.
• R. Hashemzehi, S. J. S. Mahdavi, M. Kheirabadi, and S. R. Kamel, “Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE,” Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1225–1232, Jul. 2020, doi: 10.1016/j.bbe.2020.06.001.
• A. Aziz et al., “An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification,” Computers, Materials & Continua, vol. 69, no. 2, p. 2653, Jul. 2021, doi: 10.32604/CMC.2021.018606.
• K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778, Dec. 2015, doi: 10.48550/arxiv.1512.03385.
• G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 2261–2269, Aug. 2016, doi: 10.48550/arxiv.1608.06993.
• N. F. Aurna, M. A. Yousuf, K. A. Taher, A. K. M. Azad, and M. A. Moni, “A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models,” Computers in Biology and Medicine, vol. 146, p. 105539, Jul. 2022, doi: 10.1016/J.COMPBIOMED.2022.105539.
• M. Tan and Q. v. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700, May 2019, doi: 10.48550/arxiv.1905.11946.
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 2818–2826, Dec. 2015, doi: 10.48550/arxiv.1512.00567.
• F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 1800–1807, Oct. 2016, doi: 10.48550/arxiv.1610.02357.
• N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, and M. O. Alassafi, “Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method,” Computers, Materials & Continua, vol. 67, no. 3, p. 3967, Mar. 2021, doi: 10.32604/CMC.2021.014158.
• “Brain Tumor Classification (MRI) | Kaggle.” https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri (accessed Jul. 05, 2022).