Abstract
In medical image processing, the detection, classification and segmentation of the tumor region from MRI scans accurately are very complicated, significant and time-consuming process. When there is a scenario occurs to handle with large amount of images for tumor diagnosis, there is need of an efficient and adaptive classification model to handle with the anomalous structures of human brains. The MRI brain images show the typical internal brain structure and hence help scholars and medical practitioners in accurate disease diagnosis. With that note, this paper develops a model called improved classification model for brain tumor diagnosis for appropriate classification of tumor images from input MRI images. Initially, filtering techniques are applied for preprocessing the acquired scan images and feature extraction is done with gray-level co-occurrence matrix and discrete wavelet transform equations, which produces more precise results. And, classification is done with the technique called support vector machine, in which the binary classifications are effectively done. The proposed model is evaluated under simulation, and the obtained results outperform the results of traditional brain tumor detection process based on precision, recall and processing time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Change history
21 October 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-024-10205-3
References
Akram MU, Usman A (2011) Computer aided system for brain tumor detection and segmentation. In: IEEE
Alfonse M, Salem M (2016) An automatic classification of brain tumors through MRI using support vector machine. Egypt Comput Sci J 40:11–21
Ananda RS, Thomas T (2012) Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. In: 5th International conference on biomedical engineering and informatics. IEEE
Bouattane O, Youssfi M, Raihani A (2019) Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area. Int J Biomed Imaging 2019:1758948
Cha S et al (2006) Review article: Update on brain tumor imaging: from anatomy to physiology. J Neuroradiol 27:475–487
Chaddad A (2015) Automated feature extraction in brain tumor by magnetic resonance imaging using Gaussian mixture models. Int J Biomed Imaging 2015:868031
Coatrieux G, Huang H, Shu H, Luo L, Roux C (2013) A watermarking based medical image integrity control system and an image moment signature for tampering characterization. IEEE J Biomed Health Inform 17(6):1057–1067
Cui W, Wang Y, Fan Y, Feng Y, Lei T (2013) Localized FCM clustering with spatial information for medical image segmentation and bias field estimation. Int J Biomed Imaging 2013:930301
Dhanalakshmi K, Rajamani V (2010) An efficient association rule-based method for diagnosing ultrasound kidney images. In: 2010 IEEE International conference on computational intelligence and computing research (ICCIC)
Dubey RB, Hanmandlu M, Vasikarla S (2011) Evaluation of three methods for MRI brain tumor segmentation. In: ITNG. IEEE Computer Society
El Far M, Moumoun L, Chahhou M, Gadi T, Benslimane R (2011) Comparing between data mining algorithms: “Close+, Apriori and CHARM” and “K means classification algorithm” and applying them on 3D object indexing. In: 2011 International conference on multimedia computing and systems (ICMCS), pp 1–6
Flusser J (2006) Moment invariants in image analysis. Proc World Acad Sci Eng Technol 2(11):196–201
Ion AL, Udristoiu S (2011) An experimental framework for learning the medical image diagnosis. In: Proceedings of information technology interfaces
Jose JS, Sivakami R, Uma Maheswari N, Venkatesh R (2012) An efficient diagnosis of kidney images using association rules. Int J Comput Technol Electron Eng (IJCTEE) 2(2):14–20
Joseph RP, Singh CS, Manikandan M (2014) Brain tumor MRI image segmentation and detection in image processing. Int J Res Eng Technol 3:1–5
Kalaiselvi T, Somasundaram K (2011) Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in MRI of head scans. In: Proceedings in IEEE-international symposium on humanities, science and engineering research, pp 149–154
Kavitha MS, Shanthini J, Sabitha R (2019) ECM-CSD: an efficient classification model for cancer stage diagnosis in CT lung images using FCM and SVM techniques. J Med Syst 43:73. https://doi.org/10.1007/s10916-019-1190-z
Kavitha MS, Shanthini J, Bhavadharini RM (2020) ECIDS-enhanced cancer image diagnosis and segmentation using artificial neural networks and active contour modelling. J Med Imaging Health Inform 10(2):428–434(7). https://doi.org/10.1166/jmihi.2020.2976
Kumar P, Vijayakumar B (2015) Brain tumor MR image segmentation and classification using by PCA and RBF kernel based support vector machine. Middle East J Sci Res 23(9):2106–2116
Kutlu H, Avcı E (2019) A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors 19(9):1992
Li W, Lu Z, Feng Q, Chen W (2010) Meticulous classification using support vector machine for brain images retrieval. In: 2010 International conference of medical image analysis and clinical application (MIACA)
Mustaqeem A, Javed A, Fatima T (2012) An efficient brain tumor detection algorithm using watershed and thresholding based segmentation. Int J Image Graph Signal Process 4(10):34–39
Rajendran P, Madheswaran M (2009) Pruned associative classification technique for the medical image diagnosis system. In: 2009 Second international conference on machine vision
Sabitha R, Karthik S, Shanthini J (2016) Breast cancer detection using enhanced descriptive approach. J Med Imaging Health Inform 6:1887–1892
Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK (2013) Segmentation, feature extraction, and multi class brain tumor classification. J Digit Imaging 26(6):1141–1150
Salman SD, Bahrani AA (2010) Segmentation of tumor tissue in gray medical images using watershed transformation method. Int J Adv Comput Technol 2(4):123–127
Shekhawat P, Dhande SS (2011a) Building an iris plant data classifier using neural network associative classification. Int J Adv Technol 2(4):491–506
Shekhawat PB, Dhande SS (2011b) A classification technique using associative classification. Int J Comput Appl 20(5):20–28
Shen S, Sandham WA, Granat MH (2003) Preprocessing and segmentation of brain magnetic resonance images. In: IEEE Conference on information technology applications, proceedings of the 4th annual biomedicine, UK, pp 149–152
Telrandhe SR, Pimpalkar A, Kendhe A (2015) Brain tumor detection using object labeling algorithm and SVM. Int Eng J Res Dev 2:2–8 (Special issue)
Varuna Shree N, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 5:23–30
Wang Q, Liacouras EK, Miranda E, Kanamalla US, Megalooikonomou V (2007) Classification of brain tumors using MRI and MRS. In: Proceedings of SPIE - the international society for optical engineering. https://doi.org/10.1117/12.713544
Yao J, Chen J, Chow C (2009) Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE J Sel Top Signal Process 3(1):94–100
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med Magn Reson Med 62(6):1609–1618
Zanaty EA (2012) Determination of gray matter (GM) and white matter (WM) volume in brain magnetic resonance images (MRI). Int J Comput Appl 45:16–22
Funding
This research is not supported under any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Research involving human participants and/or animal
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
All referred study is highlighted in the Literature Review.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s00500-024-10205-3
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gokulalakshmi, A., Karthik, S., Karthikeyan, N. et al. RETRACTED ARTICLE: ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifier. Soft Comput 24, 18599–18609 (2020). https://doi.org/10.1007/s00500-020-05096-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05096-z