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首页> 外文期刊>Medical hypotheses >Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware
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Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware

机译：基于极端学习机的脑肿瘤分割方法，在覆盆子PI硬件上运行显着快速鲁棒的模糊C型聚类算法

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摘要

Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.
机译：近年来，自动决策支持系统在卫生部门获得了重要性。与最近的人工智能和图像处理领域的最新进展并行，嵌入式系统也用于肿瘤诊断的决策支持系统。极端学习机（ELM）是最近开发，快速高效的算法，可以使用机器学习技术快速无瑕地诊断肿瘤。同样，显着快速且坚固的模糊C-Meary聚类算法（FRFCM）是一种新颖且快速算法，可以显示高性能。在本研究中，基于极端学习机的基于极端学习机和覆盆子PI（PRI）硬件上的显着快速且坚固的模糊C-MEARION聚类算法（BTS-ELM-FRFCM）提出了一种脑肿瘤分割方法。本研究主要旨在推出一种新的分段系统硬件，其中包含新算法，并提供高精度的卫生部门。由于其成本效益和满足硬件，PRI是有用的移动设备。 3200次培训图像用于在本研究中训练ELM。 20件MRI图像用于测试过程。 MerID（FOM），使用Jaccard相似系数（JSC）和骰子指数来评估所提出的方法的性能。此外，基于支持向量机（BTS-SVM），基于基于自组织地图的模糊C型（BTS-FCM）和脑肿瘤分割，将所提出的方法与基于支持向量机（BTS-SVM），脑肿瘤分割进行比较。基于自组织地图和K-意思是（BTS-SOM）。使用四种不同方法获得的FOM，JSC和骰子结果的统计分析表明BTS-ELM-FRFCM显示出最高性能。因此，可以得出结论，本研究中设计的嵌入式系统可以以高精度率进行脑肿瘤分割。

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