Figure 4-2
Conclusion:
Using a CNN network for brain tumor categorization, we evaluate the efficacy of the network in this study. We have found that although though a large amount of data is required to train the neural network, we can get almost 100 percent accuracy with a medium-sized dataset, according to our experiments.Our accuracy is better than ResNet-v2-152, Inception-v3, and Inception-Resnet-v2. Suggested model takes 153 seconds, compared to 300 seconds for the ResNet-v2-152, 164 seconds for the Inception-v3, and 246 seconds for the Inception-Resnet-v2. As a result, suggested model requires fewer computing parameters due to its shorter run time. Suggested model is significantly more accurate than ResNet-v2-152, Inception-v3, and Inception-Resnet-v2. Our proposed method may be predictive in diagnosing cancers in patients with brain tumors. A detailed hyperparameter configuration and a more efficient preprocessing strategy can be considered to improve the model’s efficiency further. However, in future work, the proposed method could be extended to other classification problems such as identifying brain tumors, and it could be used to detect dangerous diseases early in other medical cases areas and related to medical imaging, such as lung and breast cancer, which have extremely high global mortality rates.
[1] H. R. Almadhoun and S. S. Abu-Naser, ”Detection of Brain Tumor Using Deep Learning,” International Journal of Academic Engineering Research (IJAER), vol. 6, no. 3, 2022.
[2] S. Sajid, S. Hussain, and A. Sarwar, ”Brain tumor detection and segmentation in MR images using deep learning,” Arabian Journal for Science and Engineering, vol. 44, pp. 9249-9261, 2019.
[3] M. Siar and M. Teshnehlab, ”Brain tumor detection using deep neural network and machine learning algorithm,” in 2019 9th international conference on computer and knowledge engineering (ICCKE) , 2019: IEEE, pp. 363-368.
[4] T. Saba, A. S. Mohamed, M. El-Affendi, J. Amin, and M. Sharif, ”Brain tumor detection using fusion of hand crafted and deep learning features,” Cognitive Systems Research, vol. 59, pp. 221-230, 2020.
[5] H. M. Rai and K. Chatterjee, ”2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net,” Multimedia Tools and Applications, vol. 80, pp. 36111-36141, 2021.
[6] Z. Jia and D. Chen, ”Brain tumor identification and classification of MRI images using deep learning techniques,” IEEE Access, 2020.
[7] A. M. Alqudah, H. Alquraan, I. A. Qasmieh, A. Alqudah, and W. Al-Sharu, ”Brain tumor classification using deep learning technique–a comparison between cropped, uncropped, and segmented lesion images with different sizes,” arXiv preprint arXiv:2001.08844, 2020.
[8] N. M. Dipu, S. A. Shohan, and K. Salam, ”Deep learning based brain tumor detection and classification,” in 2021 International Conference on Intelligent Technologies (CONIT) , 2021: IEEE, pp. 1-6.
[9] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, and M. Shoaib, ”A deep learning model based on concatenation approach for the diagnosis of brain tumor,” IEEE Access, vol. 8, pp. 55135-55144, 2020.
[10] P. G. Brindha, M. Kavinraj, P. Manivasakam, and P. Prasanth, ”Brain tumor detection from MRI images using deep learning techniques,” in IOP conference series: materials science and engineering , 2021, vol. 1055, no. 1: IOP Publishing, p. 012115.
[11] A. Saleh, R. Sukaik, and S. S. Abu-Naser, ”Brain tumor classification using deep learning,” in 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech) , 2020: IEEE, pp. 131-136.
[12] V. Nair and G. E. Hinton, ”Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807-814.
[13] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016.
[14] D. P. Kingma and J. Ba, ”Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[15] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, ”Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929-1958, 2014.
[16] K. He, X. Zhang, S. Ren, and J. Sun, ”Identity mappings in deep residual networks,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14 , 2016: Springer, pp. 630-645.
[17] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, ”Rethinking the inception architecture for computer vision,” inProceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818-2826.
[18] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, ”Inception-v4, inception-resnet and the impact of residual connections on learning,” in Proceedings of the AAAI conference on artificial intelligence , 2017, vol. 31, no. 1.
[19] A. Hamada. ”Brain Tumor Dataset - Kaggle.” https://www.kaggle.com/ahmedhamada0/brain-tumor-detection?select=no (accessed.