An Automated Method for MRI Based Brain Tumor Detection using Berkeley Wavelet Transformation and Support Vector Machine
V. Vinay Kumar1, K. Sai Krishna2, S. Kusumavathi3
1V.Vinay Kumar, Department of ECE, Anurag Group of Institutions, Venkatapur, Ghatkesar, Medchal (Telagana), India.
2K.Sai Krishna, Department of ECE, Anurag Group of Institutions, Venkatapur,Ghatkesar, Medchal (Telagana), India.
3S.Kusumavathi, Department of ECE, Anurag Group of Institutions, Venkatapur,Ghatkesar, Medchal (Telagana), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1062-1065 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11750986S319/19©BEIESP | DOI: 10.35940/ijeat.F1175.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this research work, a new automated system is developed for brain tumor detection by using Magnetic Resonance Imaging (MRI) on the basis of machine learning techniques. The major concerns in the brain tumor detection are time consuming, and the classification accuracy dependsonly on clinician’s experience. To address these issues, a new supervised system is developed for brain tumor detection. In this research study, a new segmentation approach was used for improving the brain tumor detection performance and to diminish the complexity of the system. Initially, Anisotropic Diffusion Filter (ADF) was used as an image pre-processing technique for removing noise from the collected brain image. Then, Berkeley Wavelet Transformation (BWT) was utilized for converting the spatial form of pre-processed MRI image into temporal domain frequency. Besides, Support Vector Machine (SVM) was usedas a classification technique to classify the normal and abnormal regions. SVM classifier effectively diminishes the size of resulting dual issue by developing a relaxed classification error bound. In addition, the undertaken classification approach quickly speed up the training process by maintaining a competitive classification accuracy. From the experimental analysis, the proposed system improved dice coefficient >0.9 compared to the existing systems. The experimental investigation validated and evaluated that the proposed system showed good performance related to the existing systems in light of dice coefficient and accuracy.
Keywords: Support Vector Machine, Berkeley Wavelet Transformation, Anisotropic Diffusion Filter, Brain Tumor Detection MRI Images.
Scope of the Article: Aggregation, Integration, and Transformation