Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

Article Sidebar

PDF
Published: Jun 10, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.7130
Keywords:
Brain Tumors Classification, CNN, Transfer Learning

Main Article Content

Mohit Angurala
Apex Institute of Technology (CSE), Chandigarh University, Gharuan, Mohali, Punjab, India

Abstract

A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified as glioma, meningioma, and pituitary, are accounting for most brain tumors. To ensure the robustness of the suggested method, data acquisition, and preprocessing are performed in the first step followed by data augmentation. Finally, Transfer Learning algorithms including DenseNet, ResNetV2, and InceptionResNetv2 have been applied to find out the optimum algorithm based on various parameters including accuracy, precision, and recall, and are under the curve (AUC). The experimental outcomes show that the model’s validation accuracy is high for DenseNet (about 97%), while ResNetv2 and InceptionResNetv2 achieved 77% and 80% only.

Article Details

How to Cite
Angurala, M. . (2023). Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 536–542. https://doi.org/10.17762/ijritcc.v11i5s.7130
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

References

. G. W. Jiji, L. Ganesan, and S. S. Ganesh, "Unsupervised texture classification", J. Theor. Appl. Inf. Technol., vol. 5, pp. 373-381, Apr. 2009.

. K. V. Ramana and B. R. Korrapati, "A neural network based classification and diagnosis of brain hemorrhages", Int. J. Artif. Intell. Expert Syst., vol. 1, no. 2, 2009.

. P. P. R. Filho et al., "Automated recognition of lung diseases in CT images based on the optimum-path forest classifier", Neural Comput. Appl., pp. 1-14, Jun. 2017.

. R. Ayachi and N. B. Amor, "Brain tumor segmentation using support vector machines", Proc. 10th Eur. Conf. (ECSQARU), vol. 5590, pp. 736-747, 2009.

. M. T. El-Melegy and H. M. Mokhtar, "Tumor segmentation in brain MRI using a fuzzy approach with class center priors", EURASIP J. Image Video Process., vol. 21, no. 1, pp. 2-14, 2014.

. A. Demirhan, M. Törü and I. Güler, "Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks", IEEE J. Biomed. Health Inform., vol. 19, no. 4, pp. 1451-1458, Jul. 2015.

. Mark White, Thomas Wood, Maria Hernandez, María González , María Fernández. Enhancing Learning Analytics with Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/184.

. F. Xing, Y. Xie and L. Yang, "An automatic learning-based framework for robust nucleus segmentation", IEEE Trans. Med. Imag., vol. 35, no. 2, pp. 550-566, Feb. 2016.

. A. Islam, S. M. S. Reza and K. M. Iftekharuddin, "Multifractal texture estimation for detection and segmentation of brain tumors", IEEE Trans. Biomed. Eng., vol. 60, no. 11, pp. 3204-3215, Nov. 2013.

. R. Gravina, P. Alinia, H. Ghasemzadeh and G. Fortino, "Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges", Inf. Fusion, vol. 35, pp. 68-80, May 2017.

. P. M. Shakeel, S. Baskar, V. R. S. Dhulipala, S. Mishra and M. M. Jaber, "Maintaining security and privacy in health care system using learning based deep-Q-networks", J. Med. Syst., vol. 42, pp. 186, Oct. 2018.

. G. S. N. Murthy and T. Veerraju, "A novel approach based on decreased dimension and reduced gray level range matrix features for stone texture classification", Int. J. Elect. Comput. Eng., vol. 7, no. 5, pp. 2502-2513, 2017.

. Ahmed Ali, Anaïs Dupont,Deep Generative Models for Image Synthesis and Style Transfer , Machine Learning Applications Conference Proceedings, Vol 2 2022.

. F. J. Martinez-Murcia et al., "On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis", Hum. Brain Mapping, vol. 38, no. 3, pp. 1208-1223, 2017.

. Shan Shen, W. Sandham, M. Granat and A. Sterr, "MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization," in IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp. 459-467, Sept. 2005, doi: 10.1109/TITB.2005.847500.

. J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha and A. Yuille, "Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification," in IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640, May 2008, doi: 10.1109/TMI.2007.912817.

. A. Islam, S. M. S. Reza and K. M. Iftekharuddin, "Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors," in IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204-3215, Nov. 2013, doi: 10.1109/TBME.2013.2271383.

. S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, G. Fortino and M. Buckland, "A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis," in IEEE Access, vol. 4, pp. 9145-9154, 2016, doi: 10.1109/ACCESS.2016.2647238.

. G. Wu et al., "Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors," in IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 893-905, April 2018, doi: 10.1109/TMI.2017.2776967.

. P. Mohamed Shakeel, T. E. E. Tobely, H. Al-Feel, G. Manogaran and S. Baskar, "Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor," in IEEE Access, vol. 7, pp. 5577-5588, 2019, doi: 10.1109/ACCESS.2018.2883957.

. H. A. Shah, F. Saeed, S. Yun, J. -H. Park, A. Paul and J. -M. Kang, "A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet," in IEEE Access, vol. 10, pp. 65426-65438, 2022, doi: 10.1109/ACCESS.2022.3184113.

. Baser, P. ., Jatinderkumar R. Saini, & Baser, N. . (2023). Gold Commodity Price Prediction Using Tree-based Prediction Models. International Journal of Intelligent Systems and Applications in Engineering, 11(1s), 90–96. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2481.

. S. Ahmad and P. K. Choudhury, "On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images," in IEEE Access, vol. 10, pp. 59099-59114, 2022, doi: 10.1109/ACCESS.2022.3179376.

. A. Vidyarthi, R. Agarwal, D. Gupta, R. Sharma, D. Draheim and P. Tiwari, "Machine Learning Assisted Methodology for Multiclass Classification of Malignant Brain Tumors," in IEEE Access, vol. 10, pp. 50624-50640, 2022, doi: 10.1109/ACCESS.2022.3172303.

. Pise, D. P. . (2021). Bot Net Detection for Social Media Using Segmentation with Classification Using Deep Learning Architecture. Research Journal of Computer Systems and Engineering, 2(1), 11:15. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/13.

. Y. Kumar, N. P. Patel, A. Koul and A. Gupta, "Early Prediction of Neonatal Jaundice using Artificial Intelligence Techniques," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Gautam Buddha Nagar, India, 2022, pp. 222-226, doi: 10.1109/ICIPTM54933.2022.9753884.

. S. Chandrasekaran, V. Dutt, N. Vyas and A. Anand, "Fuzzy KNN Implementation for Early Parkinson's Disease Prediction," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 896-901, doi: 10.1109/ICCMC56507.2023.10083522.

. R. Goel, D. K. Arora, V. Kumar and M. Mittal, "A Machine Learning based Medical Chatbot for detecting diseases," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Gautam Buddha Nagar, India, 2022, pp. 175-181, doi: 10.1109/ICIPTM54933.2022.9754016.

. A. Shome, A. Jain, V. Choudhary, P. Garg, R. Chopra and D. Vii, "Driver Drowsiness Detection System using DLib," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 193-197, doi: 10.1109/ICACITE53722.2022.9823848.