Brain Tumor Classification in MRI Images Using Genetic Algorithm Appended CNN
Brain tumors are fatal for majority of the patients, the different nature of the tumor cells requires the use of combined medical measures, and categorizing such tumors is a difficult task for radiologists. The diagnostic structures based on PCs have been offered as an aid in diagnosing a brain tumor using magnetic resonance imaging (MRI). General functions are retrieved from the lowest layers of the neural network, and these lowest layers are responsible for capturing low-level features and patterns in the raw input data, which can be particularly unique to the raw image. To validate this, the EfficientNetB3 pre-trained model is utilized to classify three types of brain tumors: glioma, meningioma, and pituitary tumor. Initially, the characteristics of several EfficientNet modules are taken from the pre-trained EfficientNetB3 version to locate the brain tumor. Three types of brain tumor datasets are used to assess each approach. Compared to the existing deep learning models, the concatenated functions of EfficientNetB3 and genetic algorithms give better accuracy. Tensor flow 2 and Nesterov-accelerated adaptive moment estimation (Nadam) are also employed to improve the model training process by making it quicker and better. The proposed technique using CNN attains an accuracy of 99.56%, a sensitivity of 98.9%, a specificity of 98.6%, an F-score of 98.9%, a precision of 98.9%, and a recall of 99.54%.
Keywordsdeep learning, convolutional neural networks, EfficientNetB3, genetic algorithm, brain tumor classification,
References1. H.N.T.K. Kaldera, S.R. Gunasekara, M.B. Dissanayake, Brain tumor classification and segmentation using faster R-CNN, [in:] 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, pp. 1–6, 2019, doi: 10.1109/ICASET.2019.8714263.
2. S. Iqbal, M.U. Ghani, T. Saba, A. Rehman, Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN), Microscopy Research and Technique, 81(4): 419–427, 2018, doi: 10.1002/jemt.22994.
3. S. Das, O.F.M.R.R. Aranya, N.N. Labiba, Brain tumor classification using convolutional neural network, [in:] 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1–5, 2019, doi: 10.1109/ICASERT.2019.8934603.
4. W. Zhang, Y. Wu, B. Yang, S. Hu, L. Wu, S. Dhelimd, Overview of multi-modal brain tumor MR image segmentation, Healthcare, 9(8): 1051, 2021, doi: 10.3390/healthcare9081051.
5. S. Abbasi, F. Tajeripour, Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient, Neurocomputing, 219: 526–535, 2017, doi: 10.1016/j.neucom.2016.09.051.
6. 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, 45(3): 1015–1036, 2021, doi: 10.1007/s40998-021-00426-9.
7. F.J. Díaz-Pernas, M. Martínez-Zarzuela, M. Antón-Rodríguez, D. González-Ortega, A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network, Healthcare, 9(2): 153, 2021, doi: 10.3390/healthcare9020153.
8. G. Mohan, M.M. Subashini, MRI based medical image analysis: Survey on brain tumor grade classification, Biomedical Signal Processing and Control, 39: 139–161, 2018, doi: 10.1016/j.bspc.2017.07.007.
9. L. Pei, L. Vidyaratne, M. Rahman, K.M. Iftekharuddin, Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images, Scientific Reports, 10: 19726, 2020, doi: 10.1038/s41598-020-74419-9.
10. H.A. Khan, W. Jue, M. Mushtaq, M.U. Mushtaq, Brain tumor classification in MRI image using convolutional neural network, Mathematical Biosciences and Engineering, 17(5): 6203–6216, 2020, doi: 10.3934/mbe.2020328.
11. S.A. Abdelaziz Ismael, A. Mohammed, H. Hefny, An enhanced deep learning approach for brain cancer MRI images classification using residual networks, Artificial Intelligence in Medicine, 102: 101779, 2020, doi: 10.1016/j.artmed.2019.101779.
12. F. Özyurt, E. Sert, E. Avci, E. Dogantekin, Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy, Measurement, 147: 106830, 2019, doi: 10.1016/j.measurement.2019.07.058.
13. A.K. Anaraki, M. Ayati, F. Kazemi, Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms, Biocybernetics and Biomedical Engineering, 39(1): 63–74, 2019, doi: 10.1016/j.bbe. 2018.10.004.
14. S. Kumar, D.P. Mankame, Optimization driven deep convolution neural network for brain tumor classification, Biocybernetics and Biomedical Engineering, 40(3): 1190–1204, 2020, doi: 10.1016/j.bbe.2020.05.009.
15. S. Deepak, P.M. Ameer, Brain tumor classification using deep CNN features via transfer learning, Computers in Biology and Medicine, 111: 103345, 2019, doi: 10.1016/j.compbiomed.2019.103345.
16. A.M. Sarhan, Brain tumor classification in magnetic resonance images using deep learning and wavelet transform, Journal of Biomedical Science and Engineering, 13(6): 102–112, 2020, doi: 10.4236/jbise.2020.136010.
17. R. Karakıs, M. Tez, Y.A. Kılıç, Y. Kuru, I. Güler, A genetic algorithm model based on artificial neural network for prediction of the axillary lymph node status in breast cancer, Engineering Applications of Artificial Intelligence, 26(3): 945–950, 2013, doi: 10.1016/j.engappai.2012.10.013.
18. G.R. Chandra, K.R.H. Rao, Tumor detection in brain using genetic algorithm, Procedia Computer Science, 79: 449–457, 2016, doi: 10.1016/j.procs.2016.03.058.
19. Z.N.K. Swati et al., Brain tumor classification for MR images using transfer learning and fine-tuning, Computerized Medical Imaging and Graphics, 75: 34–46, 2019, doi: 10.1016/j.compmedimag.2019.05.001.
20. N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, T.R. Mengko, Brain tumor classification using convolutional neural network, [in:] 2018 World Congress on Medical Physics and Biomedical Engineering, FMBE Proceedings, L. Lhotska, L. Sukupova, I. Lackovic, G.S. Ibbott [Eds], vol. 68/1, pp. 183–189, Springer, Singapore, 2018, doi: 10.1007/978-981-10-9035-6_33.
21. M.R. Ismael, I. Abdel-Qader, Brain tumor classification via statistical features and backpropagation neural network, [in:] 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, USA, pp. 0252–0257, 2018, doi: 10.1109/EIT.2018.8500308.
22. A. Pashaei, H. Sajedi, N. Jazayeri, Brain tumor classification via convolutional neural network and extreme learning machines, [in:] 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314–319, 2018, doi: 10.1109/ICCKE.2018.8566571.
23. N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, M. Shoaib, A deep learning model based on concatenation approach for the diagnosis of brain tumor, IEEE Access, 8: 55135–55144, 2020, doi: 10.1109/ACCESS.2020.2978629.
24. H. Mzoughi et al., Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification, Journal of Digital Imaging, 33: 903–915, 2020, doi: 10.1007/s10278-020-00347-9.
25. W. Ayadi, W. Elhamzi, I. Charfi, M. Atri, Deep CNN for brain tumor classification, Neural Processing Letters, 53: 671–700, 2021, doi: 10.1007/s11063-020-10398-2.
26. S. Pereira, R. Meier, V. Alves, M. Reyes, C.A. Silva, Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment, [in:] Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN DLF IMIMIC 2018, Lecture Notes in Computer Science, Vol. 11038, pp. 16–20, Springer, Cham, 2018, doi: 10.1007/978-3-030-02628-8_12.
27. Brain tumor dataset, 2017, https://figshare.com/articles/dataset/brain_tumor_dataset/
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