A Novel Analytical Model of Brain Tumor Based on Swarm Robotics
PDF

Keywords

Swarm robots
Brain tumor
Analytical computation
Kill chain
Interior point algorithm

DOI

10.26689/par.v6i4.4196

Abstract

A tumor is referred to as “intracranial hard neoplasm” if it grows near the brain or central spinal vessel (neoplasm). In certain cases, it is possible that the responsible cells are neurons situated deep inside the brain’s structure. This article discusses a strategy for halting the progression of brain tumor. A precise and accurate analytical model of brain tumors is the foundation of this strategy. It is based on an algorithm known as kill chain interior point (KCIP), which is the result of a merger of kill chain and interior point algorithms, as well as a precise and accurate analytical model of brain tumors. The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor. Based on the motion of swarm robots, which are considered a subset of artificial intelligence, this article proposes a new notion of this kind of behavior, which may be used in various situations. The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types. According to the findings, it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors. It is hoped that this study will help researchers better understand the behavior of brain tumors, so as to develop a new drug that is effective in eliminating the tumor cells.

References

Chiavazza C, Franchino F, Ruda R, 2019, Clinical Concepts of Brain Tumors, in Neurorehabilitation in Neuro-Oncology, Springer, Cham, 37–51.

Hou X, Yang D, Li D, et al., 2020, A New Simple Brain Segmentation Method for Extracerebral Intracranial Tumors. PLoS ONE, 15(4): e0230754.

Datta M, Nia HT, Seano G, et al., 2020, TMOD-37. In Vivo Compression and Imaging for Causal Studies of Mechanical Forces in the Brain. Neuro Oncology, 22(Suppl 2): ii235–ii236.

Li M, Shang Z, Dong Y, et al., 2017, Proceeding of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 11-15, 2017: Application of MRI Texture Analysis in the Study of the Posterior Fossa Tumors Growing Trend in Children. IEEE, Jeju, Korea (South), 620–623.

Beda MM, Barjaktarovic M, 2020, Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network. Applied Sciences, 10(6): 1999.

Nalepa J, Lorenzo PR, Marcinkiewicz M, et al., 2020, Fully-Automated Deep Learning-Powered System for DCE-MRI Analysis of Brain Tumors. Artificial Intelligence in Medicine, 102: 101769.

Chen C, Ou X, Wang J, et al., 2019, Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors. Frontiers in Oncology, 9: 806.

Badran EF, Mahmoud EG, Hamdy N, 2010, Proceedings of the 2010 International Conference on Computer Engineering & Systems, April 16-18, 2010: An Algorithm for Detecting Brain Tumors in MRI Images. IEEE, Chengdu, China, 368–373.

Narmatha C, Eljack SM, Tuka A, et al., 2020, A Hybrid Fuzzy Brain-Storm Optimization Algorithm for the Classification of Brain Tumor MRI Images. Journal of Ambient Intelligence and Humanized Computing, 2020: 1–9.

Sachdeva J, Kumar V, Gupta I, et al., 2016, A Package-SFERCB-“Segmentation, Feature Extraction, Reduction and Classification Analysis by Both SVM and ANN for Brain Tumors”. Appl Soft Comput, 47: 151–167.

Das A, Bhattacharya M, 2009, Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, August 3-5, 2009: A Study on Prognosis of Brain Tumors Using Fuzzy Logic and Genetic Algorithm Based Techniques. IEEE, Shanghai, 348–351.

Abdulraqeb A, Al-Haidri W, Sushkova LT, et al., 2017, An Automated Method for Segmenting Brain Tumors on MRI Images. Biomedical Engineering, 51: 97–101.

Kim J, Lee S, Lee G, et al., 2016, Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic Resonance (MRI) Images. Wireless Personal Communications, 89(3): 993–1008.

Hussain S, Anwar SM, Majid M, 2018, Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network. Neurocomputing, arXiv:1708.00377. https://doi.org/10.48550/arXiv.1708.00377

Alagarsamy S, Kamatchi K, Govindaraj V, et al., 2019, Multi-channeled MR Brain Image Segmentation: A New Automated Approach Combining BAT and Clustering Technique for Better Identification of Heterogeneous Tumors. Biocybernetics and Biomedical Engineering, 39(4): 1005–1035.

Alam MS, Rahman MM, Hossain MA, et al., 2019, Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm. Big Data Cogn Comput, 3(2): 27.