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A Novel Technique for identification of tumor region in MR Brain Image

机译:先生脑形象肿瘤区鉴定新技术

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Detection of brain tumor is a vital role in field of medical. Brain tumor with low grade and high grade will threaten the life of human being, Diagnosis of brain tumors are identified in the early stage can increase the survival rate of patients. Due to the multifarious structure in human brain there is a need of novel technique for better identification of tumors in the early stage. The suggested fruit fly based Interval type fuzzy c-means clustering techniques performs outstanding segmentation result for tumors with low grade and high grade. The competence of proposed techniques is tested with BRATS-SICAS (2015) dataset. Demarcated result obtained by the fruit fly based Interval type fuzzy c-means clustering algorithm deliberates better identification of tumors and compared with the ground truth obtained from BRATS-SICAS (2015) dataset. The suggested techniques deliberate an impressive sensitivity value of 98.47 % and dice score value of 96.23 % respectively which is much better than existing techniques used for segmentation. These novel techniques will aid the clinicians for accurate identification of tumors in the field of medical.
机译:脑肿瘤的检测是医学领域的重要作用。脑肿瘤具有低等级和高品位将威胁人类的生命,在早期阶段发现脑肿瘤的诊断可以提高患者的存活率。由于人类脑中的多态结构,需要新的技术,以便在早期阶段更好地鉴定肿瘤。所建议的果蝇基间间隔型模糊C-Means聚类技术对低等级和高等级的肿瘤进行了出色的分段结果。建议技术的能力与Brats-SiCAS(2015)数据集进行了测试。通过基于果蝇的间隔型模糊C-Means聚类算法获得的划分结果刻意更好地识别肿瘤,并与从Brats-Sicas(2015)数据集获得的地面真理相比。建议的技术刻意令人印象深刻的灵敏度值98.47%,骰子得分值分别为96.23%,比用于分割的现有技术好得多。这些新颖技术将有助于临床医生准确鉴定医疗领域的肿瘤。

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