DOI QR코드

DOI QR Code

Optimize KNN Algorithm for Cerebrospinal Fluid Cell Diseases

  • Soobia Saeed (Faculty of Engineering, Department of Software Engineering, Universiti Teknologi Malaysia) ;
  • Afnizanfaizal Abdullah (Faculty of Engineering, Department of Software Engineering, Universiti Teknologi Malaysia) ;
  • NZ Jhanjhi (School of Computer Science and Engineering, SCE, Taylor's University)
  • 투고 : 2024.02.05
  • 발행 : 2024.02.29

초록

Medical imaginings assume a important part in the analysis of tumors and cerebrospinal fluid (CSF) leak. Magnetic resonance imaging (MRI) is an image segmentation technology, which shows an angular sectional perspective of the body which provides convenience to medical specialists to examine the patients. The images generated by MRI are detailed, which enable medical specialists to identify affected areas to help them diagnose disease. MRI imaging is usually a basic part of diagnostic and treatment. In this research, we propose new techniques using the 4D-MRI image segmentation process to detect the brain tumor in the skull. We identify the issues related to the quality of cerebrum disease images or CSF leakage (discover fluid inside the brain). The aim of this research is to construct a framework that can identify cancer-damaged areas to be isolated from non-tumor. We use 4D image light field segmentation, which is followed by MATLAB modeling techniques, and measure the size of brain-damaged cells deep inside CSF. Data is usually collected from the support vector machine (SVM) tool using MATLAB's included K-Nearest Neighbor (KNN) algorithm. We propose a 4D light field tool (LFT) modulation method that can be used for the light editing field application. Depending on the input of the user, an objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy). These light fields' approaches can help increase the efficiency of device segmentation and light field composite pipeline editing, as they minimize boundary artefacts.

키워드

참고문헌

  1. I. Altaf, AH. Vohra and S. Shams, "Management of cerebrospinal fluid leak following posterior cranial fossa surgery," Pakistan Journal of Medical Sciences, vol.32, no.6, pp.1439-144, 2016.
  2. L.G. Alexander, A. Axel , J. Batiller, S. Eljamel , J. Gauld . P. Jones et al., "A multi-center, prospective, randomized controlled study to evaluate the use of a fibrin sealant as an adjunct to sutured dural repair," British Journal of Neurosurgery, vol.29, no.1, pp.11-17, 2015. https://doi.org/10.3109/02688697.2014.948808
  3. Saeed, Soobia, and Afnizanfaizal Abdullah, "Recognition of brain cancer and cerebrospinal fluid due to the usage of different MRI image by utilizing support vector machine," Bulletin of Electrical Engineering and Informatics, vol. 9, no.2, pp.619-625, 2020.
  4. S. Saeed and A. Abdullah, "Investigation of a Brain Cancer with Interfacing of 3-Dimensional Image Processing," In Proc. International Conference on Information Science and Communication Technology (ICISCT), Karachi, Pakistan, PP.1-12, 2019.
  5. A. H. Sin , G. Caldito, D. Smith, M. Rashidi, B. Willis and A. Nanda, "Predictive factors for dural tear and cerebrospinal fluid leakage in patients undergoing lumbar surgery," German Cancer Research Center,vol.5, no.1, pp.224-227, 2019.
  6. A. Al-Badarneh, H. Najadat and A. M. Alraziqi, "A classifier to detect tumor disease in brain MRI brain images," Research Journal of Applied Sciences, Engineering and Technology, vol. 6, no.12, pp.2264-2269, 2013.
  7. W. Wei, A.YC Chen, L. Zhao, and J. J. Corso, "Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features," International journal of computer assisted radiology and surgery, vol.9, no. 2, pp.241-253, 2014. https://doi.org/10.1007/s11548-013-0922-7
  8. S. Naz, H. Majeed, and H. Irshad, "Image segmentation using fuzzy clustering: A survey," In Proc. 6th international conference on emerging technologies (ICET), IEEE, Islamabad, Pakistan, pp. 181-186, 2010.
  9. Venkatesh and M.Judith Leo. "MRI Brain Image Segmentation and Detection Using K-NN Classification," In Proc. International Conference on Physics and Photonics Processes in Nano Sciences, India, vol. 1362, no. 1, pp. 1-6, 2019.
  10. S. Saeed, A. Abdullah and NZ Jhanjhi," Investigation of a Brain Cancer with Interfacing of 3-Dimensional Image Processing," Indian Journal of Science & Technology, vol.12, no.32, pp.1-6, 2019.
  11. S. Chowdhary, S. Damlo and M. C. Chamberlain, "Cerebrospinal Fluid Dissemination and Neoplastic Meningitis in Primary Brain Tumors," Journal of Mofitt Cancer Center, vol. 24, no.1, pp.1-16, 2017.
  12. L. A. V.D. Kleij , J. D. Bresser, J. Hendrikse, J. C. W. Siero, E. T. Petersen et al., "Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T-based brain segmentation methods, " Plos One, vol. 13, no.4, pp.1-14, 2018.
  13. K. Usman and K. Rajpoot, "Brain tumor classification from multi-modality mri using wavelets and machine learning," Pattern Analysis and Application, vol.20, no.1, pp.871-881, 2017. https://doi.org/10.1007/s10044-017-0597-8
  14. Junejo, A. Zahid, S. A. Memon, I. Z. Memon, and S. Talpur, "Brain Tumor Segmentation Using 3D Magnetic Resonance Imaging Scans." In Proc. 2018 1st International Conference on Advanced Research in Engineering Sciences (ARES), IEEE, Dubai, pp. 1-6, 2018.
  15. H. Mihara, T. Funatomi, K. Tanaka, H. Kubo, Y. Mukaigawa et al., "4D light field segmentation with spatial and angular consistencies," In Proc.2016 International Conference on Computational Photography (ICCP), IEEE, Evanston, pp. 1-8, 2016.