Brain Tumor Classification using MRI-Based Detection.

Authors

  • Ahmed Obaid Dakheel Maharshi Dayanand University, Rohtak
  • Yudhvir Singh Maharshi Dayanand University, Rohtak

Keywords:

Image classification and segmentation, Image processing, Brain tumor diagnosis, Grade I to IV

Abstract

Brain tumor classification and segmentation is highly useful to provide proper care for the patients. Identification of early disease detection can safeguard the life of the patients. The research paper has reviewed the literature with 66 previously published research papers on Image comparison for brain tumor disease using artificial intelligence based machine learning algorithms[1]. The research paper has explored the insights of image processing in detection of brain tumor images obtained from various diagnosis centers in UK and India. The fundamental working principles of deep learning concepts can generate efficient diagnosis of brain tumor disease[2]. The report also presented the experimental results conducted using MATLAB Simulink with the implementation of Convolutional Neural Networks. The results demonstrated that convolutional neural networks can perform the diagnosis with high accuracy within shortest period of time.

Downloads

Download data is not yet available.

References

J. Sen et al., Machine Learning: Algorithms, Models, and Applications. 2022.

J. Alzubi, A. Nayyar, and A. Kumar, ‘Machine Learning from Theory to Algorithms: An Overview’, J Phys Conf Ser, vol. 1142, p. 12012, Nov. 2018, doi: 10.1088/1742-6596/1142/ 1/ 012012.

G. Marquet, O. Dameron, S. Saikali, J. Mosser, and A. Burgun, ‘Grading glioma tumors using OWL-DL and NCI Thesaurus’, AMIA Annual Symposium Proceedings, vol. 2007, p. 508, 2007, Accessed: Mar. 17, 2023. [Online]. Available: /pmc/articles/ PMC2655830/

P. R. Krishnaveni and G. N. Kishore, ‘Image based group classifier for brain tumor detection using machine learning technique’, Traitement du Signal, vol. 37, no. 5, pp. 865–871, Oct. 2020, doi: 10.18280/TS.370520.

D. N. Louis et al., ‘The 2021 WHO Classification of Tumors of the Central Nervous System: a summary’, Neuro Oncol, vol. 23, no. 8, p. 1231, Aug. 2021, doi: 10.1093/NEUONC/NOAB106.

L. S. Hu, A. Hawkins-Daarud, L. Wang, J. Li, and K. R. Swanson, ‘Imaging of intratumoral heterogeneity in high-grade glioma’, Cancer Lett, vol. 477, pp. 97–106, May 2020, doi: 10.1016/ J.CANLET.2020.02.025.

A. Alksas et al., ‘A Novel System for Precise Grading of Glioma’, Bioengineering, vol. 9, no. 10, Oct. 2022, doi: 10.3390/BIOENGINEERING9100532.

S. Kaur et al., ‘Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives’, IEEE Access, vol. 8, pp. 228049–228069, 2020, doi: 10.1109/ACCESS. 2020.3042273.

(Member MUHAMMAD RIZWAN 1 and (Senior Member AND DHIYA AL-JUMEILY OBE 5, ‘IEEE Xplore Full-Text PDF’:, Jan. 1990. Accessed: Mar. 21, 2023. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718069

Muthukumar. Subramanyam, ‘An Automatic Brain Tumor Detection and Segmentation Scheme for Clinical Brain Images’, Researchgate, 2014.

J. Amin, M. Sharif, M. Raza, T. Saba, and M. A. Anjum, ‘Brain tumor detection using statistical and machine learning method’, Comput Methods Programs Biomed, vol. 177, pp. 69–79, Aug. 2019, doi: 10.1016/J.CMPB.2019.05.015.

G. Litjens et al., ‘A survey on deep learning in medical image analysis’, Med Image Anal, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/ J.MEDIA.2017.07.005.

X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, ‘A deep learning model integrating FCNNs and CRFs for brain tumor segmentation’, Med Image Anal, vol. 43, pp. 98–111, 2018, doi: https://doi.org/10.1016/j.media.2017.10.002.

T. Kotsiopoulos, P. Sarigiannidis, D. Ioannidis, and D. Tzovaras, ‘Machine Learning and Deep Learning in Smart Manufacturing: The Smart Grid Paradigm’, Comput Sci Rev, vol. 40, Dec. 2020, doi: 10.1016/j.cosrev.2020.100341.

Sung. Sajjad, Muhammad & Khan, Salman & Muhammad, Khan & Wu, Wanqing & Ullah, Amin & Baik, ‘Multi-Grade Brain Tumor Classification using Deep CNN with Extensive Data Augmentation’, J Comput Sci, 2018, doi: 30. 10.1016/j.jocs.2018.12.003.

S. Chaganti, I. Nanda, K. Pandi, T. Prudhvith, and N. Kumar, Image Classification using SVM and CNN. 2020. doi: 10.1109/ICCSEA49143. 2020. 9132851

N. Hussain Dar, Image segmentation Techniques and its application. 2020.

S. Kaur et al., ‘Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives’, IEEE Access, vol. 8, pp. 228049–228069, 2020, doi: 10.1109/ACCESS. 2020.3042273.

J. Schaaf, M. Sedlmayr, B. Sedlmayr, H.-U. Prokosch, and H. Storf, ‘Evaluation of a clinical decision support system for rare diseases: a qualitative study’, BMC Med Inform Decis Mak, vol. 21, Feb. 2021, doi: 10.1186/s12911-021-01435-8.

Kayode et al., ‘Brain Tumor - An overview of the Clinical Manifestations and Treatment’, Oct. 2020, doi: 10.17352/gjct.

P. Khan et al., ‘Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances’, IEEE Access, vol. 9, pp. 37622–37655, Feb. 2021, doi: 10.1109/ACCESS.2021.3062484.

M. Rizwan, A. Shabbir, A. R. Javed, M. Shabbir, T. Baker, and D. Al-Jumeily Obe, ‘Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network’, IEEE Access, vol. 10, pp. 29731–29740, 2022, doi: 10.1109/ACCESS.2022.3153108

D. Seema and T. Christy Bobby, ‘A Review on Image Processing Approaches for Glioma Grading’, 2020 6th International Conference on Bio Signals, Images, and Instrumentation, ICBSII 2020, Feb. 2020, doi: 10.1109/ICBSII49132. 2020.9167556.

(Member MUHAMMAD RIZWAN 1 and AND DHIYA AL-JUMEILY OBE 5, ‘IEEE Xplore Full-Text PDF’:, IEEE, Jan. 20, 2022. https:// ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9718069 (accessed Mar. 21, 2023).

N. Goyal and B. Sharma, ‘Image processing techniques for brain tumor identification’, IOP Conf Ser Mater Sci Eng, vol. 1022, no. 1, Jan. 2021, doi: 10.1088/1757-899X/1022/1/012011.

J. Sangeetha, D. Vaishnavi, and J. Premalatha, ‘Comparative analysis of glioma tumor in brain using machine learning and deep learning techniques’, J Phys Conf Ser, vol. 1767, no. 1, Feb. 2021, doi: 10.1088/1742-6596/1767/1/012040.

‘Glioma Classification of MR Brain Tumor Employing Machine Learning | Request PDF’.

https://www.researchgate.net/publication/335340018_Glioma_Classification_of_MR_Brain_Tumor_Employing_Machine_Learning (accessed Mar. 21, 2023).

M. L. Giger, ‘Machine Learning in Medical Imaging’, Journal of the American College of Radiology, vol. 15, no. 3, pp. 512–520, Mar. 2018, doi: 10.1016/J.JACR.2017.12.028.

K. Abdel Razek et al., ‘Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging’, Insights Imaging, vol. 12, no. 1, Dec. 2021, doi: 10.1186/S13244-021-01102-6.

J. Amin, M. Sharif, A. Haldorai, M. Yasmin, and R. S. Nayak, ‘Brain tumor detection and classification using machine learning: a comprehensive survey’, Complex and Intelligent Systems, vol. 8, no. 4, pp. 3161–3183, Aug. 2022, doi: 10.1007/S40747-021-00563-Y.

S. Merkaj et al., ‘Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities’, Cancers (Basel), vol. 14, no. 11, Jun. 2022, doi: 10.3390/CANCERS14112623.

S. Ebrahimian et al., ‘FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies’, Acad Radiol, vol. 29, no. 4, pp. 559–566, Apr. 2022, doi: 10.1016/J.ACRA. 2021.09.002.

L. Jekel et al., ‘Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review’, Cancers (Basel), vol. 14, no. 6, p. 1369, Mar. 2022, doi: 10.3390/ CANCERS14061369/S1.

Krauze, Y. Zhuge, R. Zhao, E. Tasci, and K. Camphausen, ‘AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models’, Journal of biotechnology and biomedicine, vol. 5, no. 1, p. 1, 2022, doi: 10.26502/JBB.2642-91280046.

V. Chauhan, R. Oza, and M. A. Mehta, ‘Detection and Localization of High-Grade Glioma Brain Tumor using Fusion of MRI and MRSI’, INDICON 2022 - 2022 IEEE 19th India Council International Conference, 2022, doi: 10.1109/INDICON56171. 2022.10039883.

N. A. Samee et al., ‘Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model’, Diagnostics, vol. 12, no. 10, Oct. 2022, doi: 10.3390/DIAGNOSTICS12102541.

J. Seetha and S. S. Raja, ‘Brain tumor classification using Convolutional Neural Networks’, Biomedical and Pharmacology Journal, vol. 11, no. 3, pp. 1457–1461, Sep. 2018, doi: 10.13005/ BPJ/1511.

K. Shantta and O. Basir, ‘Brain Tumor Detection and Segmentation: A Survey’, IRA-International Journal of Technology & Engineering (ISSN 2455-4480), vol. 10, p. 55, May 2020, doi: 10.21013/jte.v10.n4.p1.

Rehman, Saad & Ajmal, Hina & Farooq, Umar & Ain, Qurrat & Riaz, Farhan & Hassan, ‘Convolutional neural network based image segmentation: a review’, 2018, doi: 26. 10.1117/ 12.2304711.

Rehman, Saad & Ajmal, Hina & Farooq, Umar & Ain, Qurrat & Riaz, Farhan & Hassan, ‘Convolutional neural network based image segmentation: a review’, 2018, doi: 26. 10.1117/12.2304711.

Hamamci, N. Kucuk, K. Karaman, K. Engin, and G. Unal, ‘Tumor-cut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications’, IEEE Trans Med Imaging, vol. 31, no. 3, pp. 790–804, Mar. 2012, doi: 10.1109/TMI.2011.2181857.

S. Bauer, C. May, D. Dionysiou, G. Stamatakos, P. Buchler, and M. Reyes, ‘Multiscale modeling for image analysis of brain tumor studies’, IEEE Trans Biomed Eng, vol. 59, no. 1, pp. 25–29, Jan. 2012, doi: 10.1109/TBME.2011.2163406.

M. S. I. Khan et al., ‘Accurate brain tumor detection using deep convolutional neural network’, Comput Struct Biotechnol J, vol. 20, p. 4733, Jan. 2022, doi: 10.1016/J.CSBJ. 2022.08.039.

E. I. Zacharaki et al., ‘Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme’, Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 62, no. 6, p. 1609, 2009, doi: 10.1002/MRM.22147.

‘Brain Cancer Staging: Grading of Central Nervous System Tumors’. https://emedicine. medscape.com/ article/2006770-overview (accessed Mar. 21, 2023).

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, vol. 45, no. 3, pp. 1015–1036, Sep. 2021, doi: 10.1007/S40998-021-00426-9/TABLES/11.

G. Osborn, D. N. Louis, T. Y. Poussaint, L. L. Linscott, and K. L. Salzman, ‘The 2021 World Health Organization Classification of Tumors of the Central Nervous System: What Neuroradiologists Need to Know’, American Journal of Neuroradiology, vol. 43, no. 7, pp. 928–937, Jun. 2022, doi: 10.3174/AJNR.A7462.

G. A. Amran et al., ‘Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network’, Electronics 2022, Vol. 11, Page 3457, vol. 11, no. 21, p. 3457, Oct. 2022, doi: 10.3390/ELECTRONICS11213457.

D. Jaswal, S. V, and K. P. Soman, ‘Image Classification Using Convolutional Neural Networks’, Int J Sci Eng Res, vol. 5, no. 6, pp. 1661–1668, Jun. 2014, doi: 10.14299/ijser. 2014.06.002.

ul Haq, J. P. Li, S. Khan, M. A. Alshara, R. M. Alotaibi, and C. B. Mawuli, ‘DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment’, Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–14, Sep. 2022, doi: 10.1038/s41598-022-19465-1.

M. Ahmed Hamza et al., ‘Optimal and Efficient Deep Learning Model for Brain Tumor Magnetic Resonance Imaging Classification and Analysis’, Applied Sciences 2022, Vol. 12, Page 7953, vol. 12, no. 15, p. 7953, Aug. 2022, doi: 10.3390/ APP12157953.

M. Z. Islam, M. M. Islam, and A. Asraf, ‘A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images’, Inform Med Unlocked, vol. 20, p. 100412, Jan. 2020, doi: 10.1016/J.IMU. 2020.100412.

Debasish Kalita, ‘Basics of CNN in Deep Learning - Analytics Vidhya’, Data Science Blogathon, Mar. 03, 2022. https://www.analyticsvidhya. com/blog/ 2022/03/basics-of-cnn-in-deep-learning/ (accessed Dec. 23, 2022).

Downloads

Published

02.02.2024

How to Cite

Obaid Dakheel, A. ., & Singh, Y. (2024). Brain Tumor Classification using MRI-Based Detection. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 479–487. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4684

Issue

Vol. 12 No. 14s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.