Classification of Brain Tumors Using Artificial Intelligence
Öz
Brain MRI is a medical image obtained by MRI, which stands for "Magnetic Resonance Imaging". Brain MRI uses magnetic fields and radio waves to create detailed images of the brain and surrounding tissues. Today, deep learning algorithms are used to detect brain tumors or classify different brain regions. In this study, feature extraction has been performed with current deep learning models using a dataset consisting of 7023 open access images obtained from patients from various parts of the world, and the results were evaluated by training Support Vector Machine (SVM) and XGBoost models with the extracted features. In this study, 4 deep learning models, VGG16, VGG19, ResNet50 and MobileNetV2, have been used for feature extraction. In order to achieve higher performance, transfer learning method is used in this study, which allows the weights of models that are pre-trained with large data sets to be used in other models. The weights of the models trained with ImageNet were included in the study to improve performance and save time. Although the original layer structures of the models are fixed, the GlobalAveragePooling2D layer has been added to the CNN models to improve performance and generalize the features extracted from deep learning models. Brain MRI images divided into 4 classes as glioma tumor, meningioma tumor, pituitary tumor and no tumor. Auxiliary functions have been used to obtain optimum values for the parameters used for training the models. Accuracy, F1-score, precision and sensitivity metrics used to evaluate the training results. When the results are evaluated, the best performance with an F1-score of 97.87% is obtained by classifying the features extracted from the ResNet50 CNN model with Support Vector Machine (SVM).
Anahtar Kelimeler
artifical intelligence, machine learning, brain tumor mri, classification
Kaynakça
- Karamehić, S., & Jukić, S. (2023). Brain tumor detection and classification using VGG16 Deep learning algorithm and Python Imaging Library. Bioengineering Studies, 4(2), 1-13.
- Remzan, N., Hachimi, Y. E., Tahiry, K., & Farchi, A. (2023). Ensemble learning based-features extraction for brain MR images classification with machine learning classifiers. Multimedia Tools and Applications.
- Pal, S. S., Raymahapatra, P., Paul, S., Dolui, S., Chaudhuri, A. K., & Das, S. (2023). A novel brain tumor classification model using machine learning techniques. International Journal of Engineering Technology and Management Sciences, 7(2), 87–98.
- Bohra, M., & Gupta, S. (2022). Pre-trained CNN models and machine learning techniques for brain tumor analysis. In 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET) (pp. 1–6).
- Hong, J. (2021). Feasibility evaluation of brain tumor magnetic resonance imaging classification using convolutional neural network model. Journal of the Korean Society of MR Technology, 31(1), 17–23.
- Latif, G., Bashar, A., Iskandar, D. N. F. A., Mohammad, N., Brahim, G. B., & Alghazo, J. M. (2023). Multiclass tumor identification using combined texture and statistical features. Medical & Biological Engineering & Computing, 61(1), 45–59.
- Kibriya, H., Masood, M., Nawaz, M., Rafique, R., & Rehman, S. (2021). Multiclass brain tumor classification using convolutional neural network and support vector machine. Conference Proceedings, 1–4.
- Tandel, G. S., Balestrieri, A., Jujaray, T., Khanna, N. N., Saba, L., & Suri, J. S. (2020). Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Computers in Biology and Medicine, 122, 103804.
- Kumar, R., Kakarla, J., Isunuri, B., & Singh, M. (2021). Multi-class brain tumor classification using residual network and global average pooling. Multimedia Tools and Applications, 80.
- Gurkahraman, K., & Karakış, R. (2021). Veri çoğaltma kullanılarak derin öğrenme ile beyin tümörlerinin sınıflandırılması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(2), 997–1012.