Classification of Brain Tumor using Dendritic Cell-Squirrel Search Algorithm in a Parallel Environment
DOI:
https://doi.org/10.47839/ijc.22.3.3235Keywords:
Particle Swarm Optimization, Mutual Information, Rider Optimization Algorithm, Dendritic Cell Algorithm, Squirrel Search Algorithm, Parallel ProcessingAbstract
Magnetic Resonance Imaging is a vital imaging tool for detecting brain malignancies in medical diagnosis. The semantic gap between low-level visual information collected by MRI equipment and high-level information stated by the doctor, on the other hand, is the biggest stumbling block in MR image classification. Large amount of medial image data is generated through various imaging modalities. For processing this large amount of medical data, considerable period of time is required. Due to this, time complexity becomes a measure challenge in medical image analysis. As a result, this paper offers analysis for brain tumour classification method named as Dendritic Cell-Squirrel Search Algorithm-based Classifier in the parallel environment. In this paper a parallel environment is proposed. In the experimentation the input dataset is divided into datasets of equal sizes and given as the input on the multiple cores to reduce the time complexity of the algorithm. Due to this, brain tumor classification becomes faster. Here initially, pre-processing is performed applying Gaussian Filter and ROI, it improves the data quality. Subsequently segmentation is done with sparse fuzzy-c-means (Sparse FCM) for extracting statistical and texture features. Additionally, for feature selection, the Particle Rider mutual information is used, which is created by combining Particle Swarm Optimization (PSO), Rider Optimization Algorithm (ROA), and mutual information. The Dendritic Cell-SSA algorithm, which combines the Dendritic Cell Algorithm and the Squirrel Search Algorithm, is used to classify brain tumors. With a maximum accuracy of 97.79 percent, sensitivity of 97.58 percent, and specificity of 98 percent, the Particle Rider MI-Dendritic Cell-Squirrel Search Algorithm-Artificial Immune Classifier outperforms the competition. The experimental result shows that the proposed parallel technique works efficiently and the time complexity is improved up to 99.94% for Particle Rider MI-Dendritic Cell- Squirrel Search Algorithm-based artificial immune Classifier and 99.92% for Rider Optimization-Dendritic Cell –Squirrel Search Algorithm based Classifier as compared to sequential approach.
References
M.Taherdangkoo, M.Hadi Bagheri, M. Yazdi, and K. P. Andriole, “An effective method for segmentation of MR brain images using the antcolony optimization algorithm,” Journal of Digit Imaging,vol.26, pp. 1116-1123, 2013.https://doi.org/10.1007/s10278-013-9596-5.
L. P. Gagnani, K. H. Wandra and H.Chhinkaniwala, “Classification optimization using PSO-SSO based support vector machine,” Proceedings of the 3rd IEEE International Conference on Computational Intelligence and Communication Technology, 2017, pp. 1-4. https://doi.org/10.1109/CIACT.2017.7977300.
G. I. Sayed, M. Soliman and A. Ella Hassanien, “Bio-inspired swarm techniques for thermogram breast cancer detection,” Medical Imaging in Clinical Applications, vol. 651, pp. 487-506, 2016. https://doi.org/10.1007/978-3-319-33793-7_21.
S. Mitra and B. Uma Shankar, “Medical image analysis for cancer managementin natural 4 computing framework,” Journal of Information Science: An International Journal archive, vol. 306, issue C, pp. 111-131, 2015.https://doi.org/10.1016/j.ins.2015.02.015.
P. Rajesh Kumar and M. Prema Kumar, “Image fusion of mammography images using meta heuristic method particle swarm optimization (PSO),” International Journal of Applied Engineering Research, vol.11, pp. 6254-6258, 2016.
M. Bhattacharya and A. Das, “Registration of multimodality medical imaging of brain using particle swarm optimization,” Proceedings of the First International Conference on Intelligent Human Computer Interaction, Springer, 2009, pp. 131-139. https://doi.org/10.1007/978-81-8489-203-1_11.
I. Fister Jr., X.-S. Yang, I. Fister, J. Brest, D. Fister, “A brief review of nature-inspired algorithms for optimization,” Electrotechnical, vol. 80, issue 3, pp. 116-122, 2013.
G. Magna, P. Casti and M. Salmeri, “Identification of mammographyanomalies for breast cancer detection by an ensemble of classification models based onartificial immune system,” Knowledge Based Systems, vol. 101, pp 60-70, 2016. https://doi.org/10.1016/j.knosys.2016.02.019.
P. Mohankumar and B. V. Kavetha, “An artificial immune system basedinvestigation of the breast cancer classification,” International Journal of Pharmacy & Technology, vol. 8, issue4, pp. 23097-23107, 2016.
S. Valarmathy, N. SuthanthiraVanitha, “Hybrid artificial immune systems for classification on MRI brain images,” IIOABJ, vol.7, issue 9, pp. 730–739, 2016.
R. R.Chakre, D. V. Patil, “Particle rider mutual information and dendritic-squirrel search algorithm with artificial immune classifier for brain tumor classification,” The Computer Journal,vol.64, issue 12, 2021, bxab194,https://doi.org/10.1093/comjnl/bxab194.
X. Chang, Q. Wang, Y. Liu, and Y. Wang, “Sparse regularization in fuzzy c-Means for high-dimensional data clustering,”IEEE Transactions on Cybernetics, vol.47, no.9, pp.2616-2627, 2016.https://doi.org/10.1109/TCYB.2016.2627686.
J. Wang, Y. Cao, B. Li, H.-J. Kim, S. Lee, “Particle swarm optimization-based clustering algorithm with mobilesink for WSNs,” Future Generation Computer Systems, vol.76, pp.452-457, 2017.https://doi.org/10.1016/j.future.2016.08.004.
D. Binu and B. S Kariyappa, “RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits,” IEEE Transactions on Instrumentation and Measurement, vol.68, no.1, pp.2-26, 2018.https://doi.org/10.1109/TIM.2018.2836058.
E. G. Learned-Miller, Entropy and Mutual Information, Department of Computer Science, University of Massachusetts, Amherst, Tech. Rep. 1-4, September 2013.
M. Karakose, “Reinforcement learning based AIC,”The Scientific World Journal, vol. 2013, article ID 581846, 2013. https://doi.org/10.1155/2013/581846.
J.Greensmith, U. Aickelin, and S.Cayzer, “Detecting danger: The dendritic cell algorithm,”In Robust Intelligent Systems, Springer, 2008,pp. 89-112.https://doi.org/10.1007/978-1-84800-261-6_5.
M. Jain, V. Singh, and A. Rani, “A novel nature-inspired algorithm for optimization: Squirrel search algorithm,”Swarm and Evolutionary Computation, 2018, pp.1–28. https://doi.org/10.1016/j.swevo.2018.02.013.
BraTS datasets. [Online]. Available at:https://www.smir.ch/BRATS/Start2015.
X. Chang, Q. Wang, Y. Liu, and Y. Wang, “Sparse regularization in fuzzy c-Means for high-dimensional data clustering,” IEEE Transactions on Cybernetics, vol.47, no.9, pp.2616-2627, 2016.https://doi.org/10.1109/TCYB.2016.2627686.
Y.-H. Jia, J. Qiu, Z.-Z. Ma, and F.-F. Li, “A novel crow swarm optimization algorithm (CSO) coupling particle swarm optimization (PSO) and crow search algorithm (CSA),”Computational Intelligence and Neuroscience, vol. 2021, 2021. https://doi.org/10.1155/2021/6686826.
D. Binu and B. S Kariyappa,“RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits,” IEEE Transactions on Instrumentation and Measurement, vol.68, no.1, pp.2-26, 2018.https://doi.org/10.1109/TIM.2018.2836058.
J.Greensmith, U.Aickelin, and S.Cayzer, “Detecting danger: The dendritic cell algorithm,”In Robust Intelligent Systems, Springer, 2008, pp. 89-112. https://doi.org/10.1007/978-1-84800-261-6_5.
M. Jain, V. Singh, and A. Rani, “A novel nature-inspired algorithm for optimization: Squirrel searchalgorithm,” Swarm and Evolutionary Computation, 2018, pp.1–28.https://doi.org/10.1016/j.swevo.2018.02.013.
S. F. Hussain, A. Pervez, M. Hussain, “Co-clustering optimization using artificial bee colony (ABC) algorithm,”Applied Soft Computing, vol. 97, Part B, 2020.https://doi.org/10.1016/j.asoc.2020.106725.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
