降低脑癌分级计算时间和内存利用率的优化算法

Deepak V.K, S. R
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引用次数: 0

摘要

临床MRI扫描在包括脑癌在内的几种严重疾病的诊断程序和患者的后续用药程序中起着重要作用。由于大脑是人体脆弱、复杂而又至关重要的部分,它是癌症患者死亡的最常见原因之一。然而,良好和及时的治疗可以在一定程度上挽救生命。因此,本文提出了一种有效的脑肿瘤识别框架,采用模糊c均值聚类的变形模型(DMFCM)、超像素分割自适应聚类(ACSP)和超像素分割自适应聚类的灰狼优化(GWO_ACSP),主要在含有高级别和低级别星形细胞瘤图像的CANCER IMAGE ACHRCHIEVE (CIA)数据库和BRATS 2015上进行了测试。在评价矩阵计算中,与RG、PFCM、SLPSO、MRG等模型相比,提出的基于灰狼优化的ACSP (GWO_ACSP)对脑肿瘤的分割准确率为0.99%。所提出算法的计算时间减少到80%,实际使用的程序内存利用率约为300%,与其他著名方法相比,具有显著的低值:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized Algorithm for Lowering the Computation Time and Memory Utilization for Grading of Brain Cancers
Clinical MRI scanning serves an essential part in the diagnostic procedure of several severe disorders including brain cancers and the following medication procedures of a patient. Because the brain is a fragile, intricate, and crucial part of the human body, it is one of the most common causes of death among cancer patients. However, a good and prompt treatment may save lives to a certain degree. Hence, in this publication, an effective brain tumor identification framework is suggested using a Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP), and Gray Wolf Optimization with Adaptive Clustering with Super pixel Segmentation (GWO_ACSP) and are mainly tested on CANCER IMAGE ACHRCHIEVE (CIA) which is a database containing High Grade and Low-Grade astrocytoma tumor images and also with BRATS 2015. The evaluation matrices were computed in which the proposed Gray Wolf Optimization-based ACSP (GWO_ACSP) gives a better answer for brain tumor segmentation with an accuracy of 0.99% than other models like RG, PFCM, SLPSO, MRG. The computational time is reduced to 80% and program memory utilization of about 300% is actually used in the proposed algorithms which shows a remarkable lower value compared to other prominent methods:
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