Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images
Nisha Joseph1, D. Murugan2, Basil John Thomas3, Ramya A4
1Nisha Joseph*, Research Scholar, Dept. of Computer Science and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamilnadu, India.
2D. Murugan, Prof & Head, Dept. of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, India.
3Basil John Thomas, Assistant Professor, Dept. of Business Administration, Sur University College, Sultanate of Oman.
4Ramya A, Assistant Professor, B.S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3150-3157 | Volume-9 Issue-3, February 2020. | Retrieval Number: C4702029320/2020©BEIESP | DOI: 10.35940/ijeat.C4702.029320
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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: This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.
Keywords: MR Brain Tumor, CLAHE, Machine Learning Scheme, MDBUTMF , DWDLOBFG and ICNN.