Automatic detection of brain tumors using genetic algorithms with multiple stages in magnetic resonance images

• Autorzy: Annam Karthik , Kumar Sunil G. , Babu Ashok P. , Domala Narsaiah

Identyfikatory

DOI

10.14313/JAMRIS/4-2022/31

• Języki publikacji: EN

Abstrakty

EN

The field of biomedicine is still working on a solution to the challenge of diagnosing brain tumors, which is now one of the most significant challenges facing the profession. The possibility of an early diagnosis of brain cancer depends on the development of new technologies or instruments. Automated processes can be made possible thanks to the classification of different types of brain tumors by utilizing patented brain images. In addition, the proposed novel approach may be used to differentiate between different types of brain disorders and tumors, such as those that affect the brain. The input image must first undergo pre-processing before the tumor and other brain regions can be separated. Following this step, the images are separated into their respective colors and levels, and then the Gray Level Co-Occurrence and SURF extraction methods are used to determine which aspects of the photographs contain the most significant information. Through the use of genetic optimization, the recovered features are reduced in size. The cut-down features are utilized in conjunction with an advanced learning approach for the purposes of training and evaluating the tumor categorization. Alongside the conventional approach, the accuracy, inaccuracy, sensitivity, and specificity of the methodology under consideration are all assessed. The approach offers an accuracy rate greater than 90%, with an error rate of less than 2% for every kind of cancer. Last but not least, the specificity and sensitivity of each kind are higher than 90% and 50%, respectively. The usage of a genetic algorithm to support the approach is more efficient than using the other ways since the method that the genetic algorithm utilizes has greater accuracy as well as higher specificity.

Słowa kluczowe

EN

MRI brain tumor; GLCM; SURF; genetic optimization; advanced machine learning

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 4
• Strony: 36--43
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Annam Karthik

• Electronics and Communications Engineering, Institute of Aeronautical Engineering, Hyderabad, 500100, India

• https://orcid.org/0000-0002-7354-4485

autor

Kumar Sunil G.

• Department of E&TC, Sharadchandra Pawar College of Engineering, Dumberwadi (Otur), Pune, India

autor

Babu Ashok P.

• Electronics and Communications Engineering, Institute of Aeronautical Engineering, Hyderabad, 500100, India

autor

Domala Narsaiah

• Electronics and Communications Engineering, Lords Institute of Engineering and Technology, Himayathsagar, Near TSPA, Hyderabad, 500091, India

Bibliografia

• [1] C. Buckner, P.D. Brown, B.P. O’Neill, F.B. Meyer, “Central Nervous System Tumors”, Symposium on Solid Tumors, Mayo Foundation for Medical Education and Research, vol. 82, no. 10, 2007, 1271–1286.
• [2] K.P. Sridhar, S. Baskar, P.M. Shakeel, V.R.S. Dhulipala, “Developing brain abnormality recognize system using multi-objective pattern producing neural network,” J Ambient Intell Humaniz Comput, vol. 10, no. 4, 2018, 1–8.
• [3] R. Anitha and D.S.S. Raja, “Development of computer-aided approach for brain tumor detection using random forest classifier”, Int J Imaging Syst Technol, vol. 28, 2018, 48–53.
• [4] R. Grant, “Medical management of adult glioma”, in: Management of Adult Glioma in Nursing Practice. London, UK: Springer, 2019, 61–80.
• [5] D.R. Johnson, J.B. Guerin, C. Giannini, J.M. Morris, L.J. Eckel, and T.J. Kaufmann, “2016 updates to the WHO brain tumor classification system: what the radiologist needs to know”, Radiographics, vol. 37, 2019, 2164–2180.
• [6] Kalyani, G., Janakiramaiah, B., Prasad, L.V.N. et al. Efficient crowd counting model using feature pyramid network and ResNeXt. Soft Comput 25, 10497–10507 (2021). https://doi.org/10.1007/s00500-021-05993-x
• [7] S. Banerjee, S. Mitra, F. Masulli, and S. Rovetta, “Deep radiomics for brain tumor detection and classification from multi-sequence MRI”, arXiv preprint arXiv:1903.09240, 2019.
• [8] N. Nida, M. Sharif, M.U.G. Khan, M. Yasmin, S.L. Fernandes, “A framework for automatic colorization of medical imaging”, IIOAB J, vol. 7, supp. 1, 2019, 202–209.
• [9] J. Amin, M. Sharif, Y. Mussarat, T. Saba, M. Raza, “Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions”, Multimed Tools Appl, vol. 79, no. 3, 2019, 1–19.
• [10] S. Naqi, M. Sharif, M. Yasmin, S.L. Fernandes, “Lung nodule detection using polygon approximation and hybrid features from CT images”, Curr Med Imaging Rev, vol. 14, no. 1, 2018, 108–117.
• [11] A. Liaqat, M.A. Khan, J.H. Shah, M. Sharif, Y. Mussarat, S.L. Fernandes, “Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection”, J Mech Med Biol, vol. 18, no. 4, 2018, 1850038.
• [12] M. Sharif, M.A. Khan, M. Faisal, Y. Mussarat, S.L. Fernandes, “A framework for offline signature verification system: best features selection approach”, Pattern Recognit Lett, vol. 139, 2018.
• [13] Ramu, G. A secure cloud framework to share EHRs using modified CP-ABE and the attribute bloom filter. Educ Inf Technol 23, 2213–2233 (2018). https://doi.org/10.1007/s10639-018-9713-7
• [14] M. Raza, M. Sharif, M. Yasmin, M.A. Khan, T. Saba, S.L. Fernandes, “Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning”, Future Gener Comput Syst, vol. 88, 2018, 28–39.
• [15] G.J. Ansari, J.H. Shah, Y. Mussart, M. Sharif, S.L. Fernandes, “A novel machine learning approach for scene text extraction”, Future Gener Comput Syst, vol. 87, no. 10, 2018, 328–340.
• [16] M. Sharif, M. Raza, J.H. Shah, M. Yasmin, S.L. Fernandes, “An overview of biometrics methods”, in: Handbook of Multimedia Information Security: Techniques and Applications. London, UK: Springer, 2019, 15–35.
• [17] R.P. Joseph and C.S. Singh, “Brain tumor MRI image segmentation and detection in image processing", Int J Res Eng Technol, vol. 3, no. 13, 2014, 1–5.
• [18] Kalyani, G., Janakiramaiah, B., Karuna, A. et al. Diabetic retinopathy detection and classification using capsule networks. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00318-9
• [19] Solomon C. and Breckon T., Fundamental of digital image processing: a practical approach with examples in Matlab, Wiley Blackwell: Chichester, West Sussex, 2011.
• [20] N. Sauwen, M. Acou, D.M. Sima, J. Veraart, F. Maes, U. Himmelreich, et al., “Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization”, BMC Med Imaging, vol. 17, no. 1, 2017, 1–14.
• [21] D. Joshi and H. Channe, “A survey on brain tumor detection based on structural mri using machine learning and deep learning techniques”, Int J Sci pTechnol Res, vol. 9, no. 4, 2020.
• [22] M. Havaei, N. Guizard, H. Larochelle, P.M. Jodoin, “Deep learning trends for focal brain pathology psegmentation in MRI”, in: Lecture Notes in Computer Science. London, UK: Springer, 2016, 125–148.
• [23] B. Padmaja, P. V. Narasimha Rao, M. Madhu Bala and E. K. Rao Patro, "A Novel Design of Autonomous Cars using IoT and Visual Features," 2018 p2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics andCloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 18-21, doi: 10.1109/I-SMAC.2018.8653736.
• [24] C.L. Devasena and M. Hemalatha, “Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm”. Cent Eur J Comput Sci, vol. 3, no. 3, 2013, 117–128.
• [25] S. Goswami and L.K.P. Bhaiya, “Brain tumor detection using unsupervised learning based neural network”, 2013 International Conference on Communication Systems and Network Technologies, Gwalior, 2013, 573–577.
• [26] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al. “Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge”, arXiv Prepeint.arXiv:1811.02629, 2018.
• [27] D.S. Marcus, A.F. Fotenos, J.G. Csernansky, J.C. Morris, and R.L. Buckner, “Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults”, J Cogn Neurosci, vol. 22, 2010, 2677–2684. DOI: 10.1162/jocn.2009.21407
• [28] Dash, S.C.B., Mishra, S.R., Srujan Raju, K. et al. Human action recognition using a hybrid deep learning heuristic. Soft Comput 25, 13079–13092 (2021). https://doi.org/10.1007/s00500-021-06149-7
• [29] H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded Up Robust Features”, European Conference on Computer Vision, vol. 3951, 2006, 404–417.
• [30] A.S. Berahas, R.H. Byrd, and J. Nocedal, “Derivative-free optimization of noisy functions via quasi-newton methods,” SIAM J Optimiz, vol. 29, 2019, 965–993.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).