Automated Identification And Categorization Of Cerebral Stroke Lesions In MRI Through Machine Learning
Keywords:
Stroke lesion detection, MRI analysis, machine learning, cerebral stroke classificationAbstract
Stroke, a leading cause of disability and mortality worldwide, necessitates rapid and accurate diagnosis for effective treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) plays a crucial role in visualizing brain stroke lesions, but manual interpretation can be time-consuming and prone to inter-observer variability. This article presents a comprehensive overview of automated methods for the detection and classification of cerebral stroke lesions in MRI scans using various machine learning techniques. The integration of advanced image processing, feature extraction, and classification algorithms offers significant potential to enhance diagnostic efficiency, improve consistency, and support clinical decision-making. We discuss the methodologies, key algorithms, and the promising results achieved in this field, highlighting the transformative impact of machine learning on neuroimaging analysis for stroke.
References
V. Velayudhan and P. Pawha, "Stroke Imaging" [Online]. Available: https://emedicine.medscape.com/article/338385-overview.
K. W. Loo, and S. H. Gan, "Burden of Stroke In Malaysia", International Journal of Stroke, vol. 7, no. 2, pp. 165-167, 2012.
M. H. Jali, T. A. Izzuddin, Z. H. Bohari, H. Sarkawi, M. F. Sulaima, M. F. Baharom, and W. M. Bukhari, "Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques," in Advanced Computer and Communication Engineering Technology, pp. 671-682, 2015.
World Stroke Organization, "Impact of Stroke," World Stroke Organization, Feb. 28, 2025. [Online]. Available: . .
Smajlović, "Strokes in Young Adults: Epidemiology and Prevention," Vascular Health and Risk Management, vol. 11, pp. 157, 2015.
J. Peter and A. H. Justus, "Knowledge and Practices of Stroke Survivors Regarding Secondary Stroke Prevention, Khomas Region, Namibia," Journal of Medical Biomedical and Applied Sciences, vol. 1, pp. 1-13, 2016.
N. M. Saad, N. S. M. Noor, A. R. Abdullah, S. Muda, A. F. Muda, and H. Musa, "Segmentation and Classification Analysis Techniques for Stroke based on Diffusion-Weighted Images," IAENG International Journal of Computer Science, vol. 44, no. 3, 2017.
O. Maier, M. Wilms, J. von der Gablentz, U. Krämer, and H. Handels, "Ischemic Stroke Lesion Segmentation in Multi-Spectral MR Images with Support Vector Machine Classifiers," in Medical Imaging 2014: Computer-Aided Diagnosis, vol 9035, pp. 21-32, 2014.
A. Shyna and J. Deepa, "Automatic Detection and Segmentation of Ischemic Stroke Lesion from Diffusion Weighted MRI using Contourlet Transform Technique," International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 4, pp. 2335-2341, 2014.
K. Nagenthiraja, K. Mouridsen, and L. R. Ribe, "Method for Delineation of Tissue Lesions," United States Patent 9,036,878, 2015.
A. R. Mathew and P. B. Anto, "Tumor Detection and Classification Of MRI Brain Image Using Wavelet Transform And SVM," in International Conference on Signal Processing and Communication (ICSPC’17), pp. 75-78. 2017.
K. Nael and W. Kubal, "Magnetic Resonance Imaging of Acute Stroke," Magnetic Resonance Imaging Clinics of North America, vol. 24, no. 2, pp. 293-304, 2016.
K. Malhotra and D.S. Liebeskind, "Overview of Neuroimaging of Stroke" in Primer on Cerebrovascular Diseases (Second Edition), pp. 676-685, Academic Press, United Kingdom, 2017.
C. L. Hung and Y. H. Wu, "Parallel Genetic-Based Algorithm On Multiple Embedded Graphic Processing Units for Brain Magnetic Resonance Imaging Segmentation," Computers and Electrical Engineering, vol. 61, pp. 373-383, 2017.
H. Y. Wey, V. R. Desai, and T. Q. Duong, "Review of Current Imaging Methods in Stroke Research" Neurological Research, vol. 35, no. 10, pp. 1092-1102, 2013.
A. Bunevicius, H. Yuan, and W. Lin, "The Potential Roles of 18F-FDG-PET in Management of Acute Stroke Patients" BioMed Research International, vol. 2013, pp. 1-14, 2013.
C. Feng, D. Zhao, and M. Huang, "Segmentation of Stroke Lesions in Multi-spectral MR Images Using Bias Correction Embedded FCM and Three Phase Level Set" in International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 2015, pp. 1-6, 2015.
X. Lladó, A. Oliver, M. Cabezas, J. Freixenet, J. C. Vilanova, A. Quiles, L. Valls, T. L. Ramió, and À. Rovira, "Segmentation of Multiple Sclerosis Lesions in Brain MRI: A Review of Automated Approaches" Information Sciences, vol. 186, pp. 164-185, 2012.
I. Despotović, B. Goossens, and W. Philips, “MRI Segmentation of the Human Brain: Challenges, Methods, and Applications,” Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1–23, 2015, doi: https://doi.org/10.1155/2015/450341.
D. B. Birnale and S. N. Patil, "A Hybrid Technique based on Fuzzy-C-Means and SVM for Detection of Brain Tumor in MRI Images," International Journal of Engineering Science and Computing, vol. 6, no. 8, pp. 2571-2574, 2016.
S. Ahmad Radzi, M. Khalil-Hani, and R. Bakhteri, "Finger-Vein Biometric Identification Using Convolutional Neural Network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, pp. 1863-1878, 2016.
K. I. Qazi, H. K. Lam, B. Xiao, G. Ouyang, and X. Yin, "Classification Of Epilepsy Using Computational Intelligence Techniques," CAAI Transactions on Intelligence Technology, vol. 1, pp. 137-149, 2016.
I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, "Euclidean distance matrices: essential theory, algorithms, and applications," IEEE Signal Processing Magazine, vol. 32, no. 6, pp. 12-30, 2015.
J. Draisma, E. Horobeţ, G. Ottaviani, B. Sturmfels, and R. R. Thomas, "The Euclidean distance degree of an algebraic variety," Foundations of computational mathematics, vol. 16, no. 1, pp. 99-149, 2016.
J. Yang and F. Stern, "A highly scalable massively parallel fast marching method for the Eikonal equation," Journal of Computational Physics, vol. 332, pp. 333-362, 2017.
M. A. Azeez, S. N. Mazhir, and A. H. Ali, "Detection and Segmentation of Lung Cancer using Statistical Features of X-Ray Images," Int J Comput Sci Mob Comput, vol. 4, no. 2, pp. 307-313, 2015.
T. K. Kim et al., "Incremental linear discriminant analysis using sufficient spanning sets and its applications," Int. J. Comput. Vision, vol. 91, no. 2, pp. 216-232, Jan. 2011, doi: 10.1007/s11263-010-0381-3.
A. H. Ali et al., "Classification of Brain Lesion using K-Nearest Neighbor technique and Texture Analysis," J. Phys. Conf. Ser., vol. 1178, no. 1, 012018, 2019, doi: 10.1088/1742-6596/1178/1/012018.
A. Sage and P. Badura, "Intracranial hemorrhage detection in head CT using double-branch convolutional neural network, support vector machine, and random forest," Appl. Sci., vol. 10, no. 21, pp. 1-13, Oct. 2020, doi: 10.3390/app10217577.
S. A. Taie and W. Ghonaim, “A new model for early diagnosis of alzheimer’s disease based on BAT-SVM classifier,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 2, pp. 759–766, Apr. 2021, doi: https://doi.org/10.11591/eei.v10i2.2714.
N. M. Saad et al., "Automated Classification of Stroke Lesion Using Bagged Tree Classifier," IOP Conf. Ser. Mater. Sci. Eng., vol. 884, no. 1, 012078, 2020, doi: 10.1088/1757-899X/884/1/012078.
S. Y. Adam, A. Yousif, and M. B. Bashir, “Classification of Ischemic Stroke using Machine Learning Algorithms”, International Journal of Computer Applications, 149(10), pp. 26-31, 2016.
S. Gupta, A. Mishra and Menaka R, "Ischemic Stroke detection using Image processing and ANN," 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India, 2014, pp. 1416-1420, doi: 10.1109/ICACCCT.2014.7019334.
A. Subudhi, S. Sahoo, P. Biswal, and S. Sabut, “Segmentation and Classification of Ischemic Stroke using Optimized Features in Brain MRI,” Biomedical Engineering: Applications, Basis and Communications, vol. 30, no. 03, p. 1850011, May 2018, doi: https://doi.org/10.4015/s1016237218500114.
S. R. Telrandhe, A. Pimpalkar, and A. Kendhe, “Implementation of Brain Tumor Detection using Segmentation Algorithm & SVM Image Classification View project Sentiment Analysis View project Swapnil R Telrandhe Implementation of Brain Tumor Detection using Segmentation Algorithm & SVM,” Int. J. Comput. Sci. Eng., vol. 8, no. 7, pp. 278–284, 2016, [Online]. Available: . Last Accessed: September 2024.
B. Kiranmayee, “Effective Analysis of Brain Tumor Using Hybrid Data Mining Techniques,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 7, pp. 286–293, 2017.
A. Batra and G. Kaushik, “SECTUBIM : Automatic Segmentation And Classification of Tumeric Brain MRI Images using FHS ( FCM, HWT and SVM ),” Int. J. Eng. Sci. Comput., vol. 7, no. 6, pp. 13190–13194, 2017.
T. Ruba, R. Tamilselvi, M. Parisa Beham, and N. Aparna, “Accurate Classification and Detection of Brain Cancer Cells in MRI and CT Images using Nano Contrast Agents,” Biomed. Pharmacol. J., vol. 13, no. 3, pp. 1227–1237, 2020.
F. J. Díaz-Pernas, M. Martínez-Zarzuela, D. González-Ortega, and M. Antón-Rodríguez, “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,” Healthc., vol. 9, no. 2, p. 153, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Dr. Elena V. Sokolova, Dr. James O. Mensah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their articles published in this journal. All articles are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.