Brain tumor detection model based on CNN and threshold segmentation
Abstract
Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.
References
Aggarwal, M., Tiwari, A. K., Sarathi, M. P., & Bijalwan, A. (2023). An early detection and segmentation of Brain Tumor using Deep Neural Network. BMC Medical Informatics and Decision Making.23. https://doi.org/10/1186/s12911-023-02174-8.
Al-Ayyoub, M., Husari, G., Darwish , O., & Alabed, A. (2014). Machine learning approach for brain tumor detection. In Proceedings of the 3rd International Conference on Information and Communication System, Manhattan, NY, USA.
Cha, S. (2006). Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol., 27(3), 475-487.
Chaddad, A., & Tanougast, C. (2016). Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Informatics, 3(1), 53–61. https://doi.org/ 10.1007/s40708-016-0033-7.
Isin, A., Direkoglu, C., & Sah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods, Procedia Computer Science, 102, 317–324. https://doi.org/10.3390/e24060799.
Jain, J., & Singh, A. (2020). Quantum based Rivest-Shamir-Adleman (RSA) approach for Digital Forensic Reports. In Modern Physics Letters B ‘MPLB’ unpaid & Peer-Reviewed World Scientific SCI journal, ISSN (print): 0217-9849 34(6), 2050085 – 205009. https://doi.org/10.1142/S0217984920500852.
Louis , D., Ohgaki, N. H., & Wiestler, O. D. (2007). The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica, 114(2), 97–109. https://doi.org/10.1007/s00401-007-0243-4
Litjens, G., Kooi,T., & Bejnordi, B. E.( 2017). A survey on deep learning in medical image analysis. Medical Image Analysis. 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
Mishra, B., Singh, N., & Singh, R. (2014). Master-slave group based model for co-ordinator selection, an improvement of bully algorithm. In Proceedings of the 2014 International Conference on Parallel, Distributed and Grid Computing. IEEE, Solan, India. 457–460. https://doi.org/10.1155/2022/9015778.
Parveen, P. (2015). Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. In Proceedings of the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 98–102. https://doi.org/10.1109/SPIN.2015.7095308.
Patil, S., & Kirange, D. (2023). Ensemble of Deep Learning Models for Brain Tumor Detection. Procedia Computer Science, 218 2468–2479. https://doi.org/10.1016/j.procs.2023.01.222.
Rashid, E., Ansari, M. D., Gunjan, V. K., & Khan, M. (2020). Enhancement in teaching quality methodology by predicting attendance using machine learning technique. In Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough Springer, New York, NY, USA. pp. 227–235, https://doi.org/10.1007/978-3-030-38445-6_17.
Saba ,T., Sameh Mohamed , A., Affendi, M. El., Amin, J., & Sharif, M. (2020). Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research. 59, 221–230. https://doi.org/10.1016/j.cogsys.2019.09.007.
Sahu, H., & Sing, N. (2018). Software-defined storage. In Innovations in Software-Defined Networking and Network Functions Virtualization. IGI Global, Hershey, PA, USA. pp. 268–290. https://doi.org/10.3390/app9163223.
Sajjad, S. (2018). Multi-Grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Com- putational Science, pp. 174–182. https://doi.org/10.1016/j.jocs.2018.12.003.
Saleck, M. (2017). Tumor detection in mammography images using fuzzy C- means and GLCM texture features. In Proceedings of the 14th International Conference on Computer Graphics, Imaging and Visualization, 122–125. https:// doi.org/10.1109/CGIV.2017.22.
Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N., & Ahuja, C. (2016). A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors. Applied Soft Computing. 47, 151–167. http://dx.doi.org/10.1016/j.asoc.2016.05.020
Sayed, E. I., Dahshan, E. l., Mohsen, H. M., Revett, K., & Salem, A. B. M. (2014). Computer-aided diagnosis of human brain tumor through MRI. A survey and a new algorithm, Expert Systems with Applications. 41(11), 5526-5545. https://doi.org/10.1016/j.eswa.2014.01.021.
Schwartzbaum, J. A., Fisher, J. L., Aldape, K. D., & Wrensch, M. (2006). Epidemiology and molecular pathology of glioma. Nature Clinical Practice Neurology, 2(9), 494–503. https://doi.org/ 10.1038/ncpneuro0289.
Singh, N., Ahuja, N. J., & Kumar, A. (2018). A novel architecture for learner-centric curriculum sequencing in adaptive intelligent tutoring system. Journal of Cases on Information Technology, 20(3), 1–20. https://doi.org/10.4018/JCIT.2018070101
Singh, N. & Abuja, N. J. (2019). Bug model based intelligent recommender system with exclusive curriculum sequencing for learner-centric tutoring. International Journal of Web- Based Learning and Teaching Technologies. 14(4), 1–25. https://doi.org/10.4018/IJWLTT.2019100101
Singh, N., Kumar, A., & Ahuja, N. J. (2019). Implementation and evaluation of personalized intelligent tutoring system, International Journal of Innovative Technology and Exploring Engineering, 8, 46–55. https://doi.org/10.35940/ijitee.J9849.0881019
Surya, G., Narayana, K. Kolli, Ansari, M. D., & Gunjan, V. K. (2021). A traditional analysis for efficient data mining with integrated association mining into regression techniques. In Proceedings of the 3rd International Conference on Communications and Cyber Physical Engineering ICCCE 2020, Hyderabad, India. 1393–1404, https://doi.org/:10.1007/978-981-15-7961-5_127.
Tiwari, A., Srivastava, S., & Pant, M. (2020). Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131, 244–260. https://doi.org/10.1016/j.patrec.2019.11.020.
Zacharaki, E. I, Wang, S., Chawla, S., Soo, Y. D., Wolf, R., Melhem, E. R. & Davatzikos, C. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. https://doi.org/10.1155/2022/6985927.
Zargar, A. J., Singh, N., Rathee, G., & Singh, A. K. (2015). Image data-deduplication using the block truncation coding technique. In Proceedings of the 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), IEEE, Noida, India, pp. 154–158. http://dx.doi.org/10.1109/ABLAZE.2015.7154986
Copyright (c) 2023 International Academic Publishing House (IAPH)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
