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Adaptive Brain Tumor Recognition Model using the Hybrid Tumor Recognition Approach
Objectives: The brain tumor recognition is an imperative part of the computing environment based robotic operation devices. The trend of the operation theatre robots is on the escalation due to the advancement in the technology, and necessitates the high precision of the tumor region for the automatic operations with the computerized interactions and without the intervention of the doctors. Methods Statistical Analysis: This paper presents the robust model that has been proposed for the brain tumor detection and classification using the SVM based tumor region recognition and classification algorithm. Findings: The proposed model has been defined with the set of the morphological operations for the reduction of the image, which is followed by the principle component analysis (PCA) based features for the tumor region classification with SVM. The proposed model has undergone the in-depth analysis under the results and discussion section, where the proposed model has been undergone the number of experiments. The proposed model has been tested on the basis of various performance parameters and has shown efficient results in the terms of performance parameters than the existing models. Application/ Improvements: The proposed model can be clearly considered better than the existing model on the basis of the obtained results from the simulation.
Brain Tumor Localization, Hybrid Tumor Extraction Algorithm, Morphological Operations, PCA Features, SVM Classification
- Yu Y, Chen-Ping C, Ruppert G, Collins R, Nguyen D, Falcao A, Liu Y. 3D blob based brain tumor detection and segmentation in MR images. IEEE 11th International Symposium on Biomedical Imaging (ISBI); 2014. p. 1192–7.
- Tarabalka T, Yuliya Y, Charpiat G, Brucker L, Bjoern H, Menze M. Spatio-temporal video segmentation with shape growth or shrinkage constraint. IEEE Transactions on Image Processing. 2014; 23(9):3829–40.
- Moon M, Kyung W, Shen YW, Bae MS, Huang CS, Chen JH, Chang RF. Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Transactions on Medical Imaging.2013; 32(7):1191–200.
- Lo L, Chung-Ming C, Chen RT, Chang YC, Yang YW, Hung MJ, Huang CS, Chang RF. Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. IEEE Transactions on Medical Imaging. 2014; 33(7):1503–11.
- Mohapatra M, Subrajeet S, Patra D, Kumar K. Unsupervised leukocyte image segmentation using rough fuzzy clustering.ISRN Artificial Intelligence; 2012.
- Chahir C, Youssef Y, Elmoataz A. Skin-color detection using fuzzy clustering. Proceedings of the ISCCSP. 2006; 3(1):1–4.
- Nadernejad N, Ehsan E, Barari A. A novel pixon-based image segmentation process using fuzzy filtering and fuzzy c-mean algorithm. International Journal of Fuzzy Systems.2011; 13(4):350–7.
- Theml H, Diem H, Haferlach T. Color Atlas of Hematology, Thieme; 2004.
- Burnett D, Crocker J. The science of laboratory diagnosis.John Wiley & Sons: New York, NY, USA; 2005.
- Hammami M, Chahir YL, Chen D, Zighed Z, Janvier J.Détection des régions de couleur de peaudansl. Image revue RIAECA Education Hermès. 2003; 17(1):219–31.
- Prabhu, C, Moorthy SN. An improved method to overcome existing problems in brain tumor segmentation. Indian Journal of Science and Technology. 2016 May; 9(19).
- Shenbagarajan, A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques.Indian Journal of Science and Technology. 2016 Jan; 9(1).
- Padmapriya, Manikandan PK, Jeyanthi K, Renuga V, Sivaraman J. Detection and classification of brain tumor using radial basis function. Indian Journal of Science and Technology. 2016 Jan; 9(1).
- Baraiya N, Modi H. Comparative study of different methods for brain tumor extraction from MRI images using image processing. Indian Journal of Science and Technology. 2016 Jan; 9(4).
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