A Hybrid Technique to Predict Brain Tumour using MRI Image

J. Kishore Kumar; Prof S. Ramakrishna

doi:10.32628/CSEIT2410326

Authors

J. Kishore Kumar Department of Computer Science, S.G.Govt.Degree College, Piler, Andhra Pradesh, India Author
Prof S. Ramakrishna Department of Computer Science, S.V University, Tirupathi, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2410326

Keywords:

Convolutional Neural Network, ResNet150, U-net, Deep Learning, MRI images

Abstract

Currently, the radiologist can more accurately identify brain tumours through the development of Computer-Assisted Diagnosis (CAD), Machine Learning and Deep Learning. Recently, Deep Learning (DL) strategies have gained traction as a means to rapidly and accurately construct automated systems for diagnosing and segmenting the image. The standard approach to this issue is to create a custom feature for classification. Most neurological diseases originate from abnormal growth of brain cells, which can compromise brain architecture and even lead to malignant brain tumours. Brain tumour detection and classification algorithms that are both quick and accurate have been the subject of extensive study. This facilitates the straight forward diagnosis of brain tumours using Magnetic Resonance Image (MRI) images. Through Deep Learning (DL) model the diagnosis of brain malignancies in MRI images using Convolutional Neural Network (CNN) is possible by training the data. So, in this paper the brain tumouris predicted byproposing a Hybridfeature extraction technique i.e., tuned CNN model with ResNet150 and U-net.

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References

Qureshi, S.A.; Raza, S.E.A.; Hussain, L.; Malibari, A.A.; Nour, M.K.; Rehman, A.U.; Al-Wesabi, F.N.; Hilal, A.M. Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection. Appl. Sci. 2022, 12, 3715. DOI: https://doi.org/10.3390/app12083715

Zahoor, M.M.; Qureshi, S.A.; Bibi, S.; Khan, S.H.; Khan, A.; Ghafoor, U.; Bhutta, M.R. A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI. Sensors 2022, 22, 2726. DOI: https://doi.org/10.3390/s22072726

Arabahmadi, M.; Farahbakhsh, R.; Rezazadeh, J. Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging. Sensors 2022, 22, 1960. DOI: https://doi.org/10.3390/s22051960

Tandel, G.S.; Biswas, M.; Kade, O.G.; Tiwari, A.; Suri, H.S.; Turk, M.; Laird, J.R.; Asare, C.K.; Ankrah, A.A.; Khanna, N.; et al. A review on a deep learning perspective in brain cancer classification. Cancers 2019, 11, 111. DOI: https://doi.org/10.3390/cancers11010111

Gore, D.V.; Deshpande, V. Comparative study of various techniques using deep Learning for brain tumor detection. In Proceedings of the 2020 IEEE International Conference for Emerging Technology (INCET), Belgaum, India, 5–7 June 2020; pp. 1–4. DOI: https://doi.org/10.1109/INCET49848.2020.9154030

DeAngelis, L.M. Brain tumors. N. Engl. J. Med. 2001, 344, 114–123. DOI: https://doi.org/10.1056/NEJM200101113440207

Borole, V.Y.; Nimbhore, S.S.; Kawthekar, D.S.S. Image processing techniques for brain tumor detection: A review. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 2015, 4, 2.

Amin, J.; Sharif, M.; Yasmin, M.; Fernandes, S.L. Big data analysis for brain tumor detection: Deep convolutional neural networks. Future Gener. Comput. Syst. 2018, 87, 290–297. DOI: https://doi.org/10.1016/j.future.2018.04.065

Iorgulescu, J.B.; Sun, C.; Neff, C.; Cioffi, G.; Gutierrez, C.; Kruchko, C.; Ruhl, J.; Waite, K.A.; Negoita, S.; Hofferkamp, J.; et al. Molecular biomarker-defined brain tumors: Epidemiology, validity, and completeness in the United States. Neuro-Oncology 2022, 24, 1989–2000. DOI: https://doi.org/10.1093/neuonc/noac113

Mabray, M.C.; Barajas, R.F.; Cha, S. Modern brain tumor imaging. Brain Tumor Res. Treat. 2015, 3, 8–23. DOI: https://doi.org/10.14791/btrt.2015.3.1.8

Cha, S. Update on brain tumor imaging: From anatomy to physiology. Am. J. Neuroradiol. 2006, 27, 475–487.

Ranjbarzadeh, R.; BagherianKasgari, A.; JafarzadehGhoushchi, S.; Anari, S.; Naseri, M.; Bendechache, M. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Rep. 2021, 11, 10930. DOI: https://doi.org/10.1038/s41598-021-90428-8

Tiwari, P.; Pant, B.; Elarabawy, M.M.; Abd-Elnaby, M.; Mohd, N.; Dhiman, G.; Sharma, S. CNN Based Multiclass Brain Tumor Detection Using Medical Imaging. Comput. Intell. Neurosci. 2022, 2022, 1830010. DOI: https://doi.org/10.1155/2022/1830010

Anaya-Isaza, A.; Mera-Jiménez, L. Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging. IEEE Access 2022, 10, 23217–23233. DOI: https://doi.org/10.1109/ACCESS.2022.3154061

Lotlikar, V.S.; Satpute, N.; Gupta, A. Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. Curr. Med. Imaging 2022, 18, 604–622. DOI: https://doi.org/10.2174/1573405617666210923144739

Xie, Y.; Zaccagna, F.; Rundo, L.; Testa, C.; Agati, R.; Lodi, R.; Manners, D.N.; Tonon, C. Convolutional neural network techniques for brain tumor classification (from 2015 to 2022): Review, challenges, and future perspectives. Diagnostics 2022, 12, 1850. DOI: https://doi.org/10.3390/diagnostics12081850

Mamun, M.; Mahmud, M.I.; Hossain, M.I.; Islam, A.M.; Ahammed, M.S.; Uddin, M.M. Vocal Feature Guided Detection of Parkinson’s Disease Using Machine Learning Algorithms. In Proceedings of the 2022 IEEE 13th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference (UEMCON), New York, NY, USA, 26–29 October 2022; pp. 566–572. DOI: https://doi.org/10.1109/UEMCON54665.2022.9965732

Mamun, M.; Uddin, M.M.; Kumar Tiwari, V.; Islam, A.M.; Ferdous, A.U. MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases? In Proceedings of the 2022 IEEE 13th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference (UEMCON), New York, NY, USA, 26–29 October 2022; pp. 561–565. DOI: https://doi.org/10.1109/UEMCON54665.2022.9965714

Mahmud, M.I.; Mamun, M.; Abdelgawad, A. A Deep Analysis of Textual Features Based Cyberbullying Detection Using Machine Learning. In Proceedings of the 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Maidu, Egypt, 18–21 December 2022; pp. 166–170. DOI: https://doi.org/10.1109/GCAIoT57150.2022.10019058

Usman, K.; Rajpoot, K. Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal. Appl. 2017, 20, 871–881. DOI: https://doi.org/10.1007/s10044-017-0597-8

Shao, C.; Yang, Y.; Juneja, S.; GSeetharam, T. IoT data visualization for business intelligence in corporate finance. Inf. Process. Manag. 2022, 59, 102736. DOI: https://doi.org/10.1016/j.ipm.2021.102736

Dhankhar, A.; Juneja, S.; Juneja, A.; Bali, V. Kernel parameter tuning to tweak the performance of classifiers for identification of heart diseases. Int. J. E-Health Med. Commun. (IJEHMC) 2021, 12, 1–16. DOI: https://doi.org/10.4018/IJEHMC.20210701.oa1

Chaplot S., Patnaik L.M., Jagannathan N. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control. 2006;1(1):86–92. DOI: https://doi.org/10.1016/j.bspc.2006.05.002

El-Dahshan E.-S.A., Hosny T., Salem A.-B.M. Hybrid intelligent techniques for mri brain images classification. Digital Signal Process. 2010;20(2):433–441. DOI: https://doi.org/10.1016/j.dsp.2009.07.002

W. Chen, B. Liu, S. Peng, J. Sun, X. Qiao, Computer-aided grading of gliomas combining automatic segmentation and radiomics, Int J Biomed Imaging 2018. DOI: https://doi.org/10.1155/2018/2512037

Srikanth B., Suryanarayana S.V. Multi-Class classification of brain tumor images using data augmentation with deep neural network. Mater. Today Proc. 2021 doi: 10.1016/j.matpr.2021.01.601. DOI: https://doi.org/10.1016/j.matpr.2021.01.601

Tandel G.S., Balestrieri A., Jujaray T., Khanna N.N., Saba L., Suri J.S. Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput. Biol. Med. 2020;122:103804. doi: 10.1016/j.compbiomed.2020.103804. DOI: https://doi.org/10.1016/j.compbiomed.2020.103804

Deepak S., Ameer P. Brain tumor classification using deep CNN features via transfer learning. Comput. Biol. Med. 2019;111:103345. doi: 10.1016/j.compbiomed.2019.103345. DOI: https://doi.org/10.1016/j.compbiomed.2019.103345