Res-Net-VGG19: Improved tumor segmentation using MR images based on Res-Net architecture and efficient VGG gliomas grading

IF 2.2 Q2 ENGINEERING, MULTIDISCIPLINARY Applications in engineering science Pub Date : 2023-10-05 DOI:10.1016/j.apples.2023.100153
Amine Ben Slama , Hanene Sahli , Yessine Amri , Hedi Trabelsi
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引用次数: 0

Abstract

Background

The determination of area tumor presents the chief challenge in brain tumor therapy and assessment. Without ionizing radiation, the medical Magnetic Resonance Imaging (MRI) tool has appeared as an essential diagnostic technique for brain cancers. Using 2D MRI images, manual segmentation of brain tumor size is a slow, error-prone task which the performance is extremely depends on operator's experience. In that respect, a consistent totally automated segmentation approach for the brain tumor detection is effectively needed to get a proficient dimension of the tumor size.

Results

In this paper, an effusively computerized scheme for brain tumor detection is proposed by the use of deep convolutional networks. The proposed method was appraised on Brain Tumor Image Segmentation (BRATS 2020) datasets, including 1352 affected by brain tumor.

Conclusion

Cross-validation technique has revealed that our process can attain talented segmentation competently reaching higher accuracy compared to other previous studies.

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Res-Net-VGG19:基于Res-Net架构和有效VGG胶质瘤分级的MR图像改进肿瘤分割
背景区域肿瘤的确定是脑肿瘤治疗和评估的主要挑战。在没有电离辐射的情况下,医学磁共振成像(MRI)工具已成为脑癌的重要诊断技术。使用2D MRI图像,手动分割脑肿瘤大小是一项缓慢、容易出错的任务,其性能在很大程度上取决于操作员的经验。在这方面,有效地需要用于脑肿瘤检测的一致的全自动分割方法来获得肿瘤大小的熟练维度。结果本文提出了一种利用深度卷积网络进行脑肿瘤检测的计算机化方案。该方法在脑肿瘤图像分割(BRATS 2020)数据集上进行了评估,包括1352个受脑肿瘤影响的数据集。结论交叉验证技术表明,与以往的其他研究相比,我们的方法可以很好地实现天才分割,达到更高的精度。
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来源期刊
Applications in engineering science
Applications in engineering science Mechanical Engineering
CiteScore
3.60
自引率
0.00%
发文量
0
审稿时长
68 days
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