In this study, a deep learning-based brain tumor detection model is proposed using a Convolutional Neural Network (CNN) architecture, specifically the ResNet18 model. The aim is to develop an automated and accurate system capable of detecting brain tumors from MRI images, classifying them into two categories: “tumor present” and “no tumor.” To enhance performance and reduce the need for large-scale annotated medical datasets, the model employs transfer learning by initializing with pre-trained weights from the ImageNet dataset. The final fully connected layers of the ResNet18 network are fine-tuned to adapt to the specific binary classification task. The MRI dataset is divided into training and test sets, and preprocessing steps such as image resizing and normalization are applied to standardize inputs. After training for ten epochs, the model achieved promising results, including an accuracy of 84.31%, a precision of 79.31%, a recall of 92.00%, and an F1 score of 85.19%. These metrics indicate the model’s robustness in detecting tumors with high sensitivity and specificity. The experimental results suggest that the proposed method can effectively extract and interpret critical features from MRI scans, offering a reliable tool for assisting radiologists in early diagnosis and reducing the risk of human error in clinical decision-making.
Ayadi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021). Deep CNN for brain tumor classification. Neural Processing Letters, 53, 671–700. https://doi.org/10.1007/s11063-020-10398-2
Xie, Y., Zaccagna, F., Rundo, L., Testa, C., Agati, R., Lodi, R., ... & Tonon, C. (2022). Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): Review, challenges, and future perspectives. Diagnostics, 12(8), 1850. https://doi.org/10.3390/diagnostics12081850
Khan, H. A., Jue, W., Mushtaq, M., & Mushtaq, M. U. (2020, September 15). Brain tumor classification in MRI image using convolutional neural network. Mathematical Biosciences and Engineering, 17(5), 6203–6216. https://doi.org/10.3934/mbe.2020328
Agarwal, A. K., Sharma, N., & Jain, M. K. (2021, January). Brain tumor classification using CNN. Advances and Applications in Mathematical Sciences, 20(3), 397–407.
Ait Amou, M., Xia, K., Kamhi, S., & Mouhafid, M. (2022, March). A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization. In Healthcare (Vol. 10, No. 3, p. 494). MDPI. https://doi.org/10.3390/healthcare10030494
Díaz-Pernas, F. J., Martínez-Zarzuela, M., Antón-Rodríguez, M., & González-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare, 9(2), 153. https://doi.org/10.3390/healthcare9020153
Ayadi, W., Elhamzi, W., & Atri, M. (2020, December). A new deep CNN for brain tumor classification. In 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (pp. 266–270). IEEE. https://doi.org/10.1109/STA50679.2020.9329328
Deepak, S., & Ameer, P. M. (2021). Automated categorization of brain tumor from MRI using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8357–8369. https://doi.org/10.1007/s12652-020-02568-w
Irmak, E. (2021). 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, 45(3), 1015–1036. https://doi.org/10.1007/s40998-021-00426-9
Mondal, A., & Shrivastava, V. K. (2022). A novel parametric flatten-p Mish activation function based deep CNN model for brain tumor classification. Computers in Biology and Medicine, 150, 106183. https://doi.org/10.1016/j.compbiomed.2022.106183
Hussain, T., Ullah, A., Haroon, U., Muhammad, K., & Baik, S. W. (2021). A comparative analysis of efficient CNN-based brain tumor classification models. In Generalization with Deep Learning: For Improvement on Sensing Capability (pp. 259–278). https://doi.org/10.1142/9789811218842_0011
Tazin, T., Sarker, S., Gupta, P., Ayaz, F. I., Islam, S., Monirujjaman Khan, M., ... & Alshazly, H. (2021). [Retracted] A robust and novel approach for brain tumor classification using convolutional neural network. Computational Intelligence and Neuroscience, 2021(1), 2392395. https://doi.org/10.1155/2021/2392395
Saleh, A., Sukaik, R., & Abu-Naser, S. S. (2020, August). Brain tumor classification using deep learning. In 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech) (pp. 131–136). IEEE. https://doi.org/10.1109/iCareTech49914.2020.00032
Haj Hashem Khani,M. M. and Maleki Nodehi,F. (2024). Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning. Transactions on Machine Intelligence, 7(4), 269-275. doi: 10.47176/TMI.2024.269
MLA
Haj Hashem Khani,M. M. , and Maleki Nodehi,F. . "Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning", Transactions on Machine Intelligence, 7, 4, 2024, 269-275. doi: 10.47176/TMI.2024.269
HARVARD
Haj Hashem Khani M. M., Maleki Nodehi F. (2024). 'Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning', Transactions on Machine Intelligence, 7(4), pp. 269-275. doi: 10.47176/TMI.2024.269
CHICAGO
M. M. Haj Hashem Khani and F. Maleki Nodehi, "Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning," Transactions on Machine Intelligence, 7 4 (2024): 269-275, doi: 10.47176/TMI.2024.269
VANCOUVER
Haj Hashem Khani M. M., Maleki Nodehi F. Brain Tumor Detection in MRI Images Using ResNet18 Convolutional Neural Network and Transfer Learning. Trans. Mach. Intell., 2024; 7(4): 269-275. doi: 10.47176/TMI.2024.269