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首页> 外文会议>NAFOSTED Conference on Information and Computer Science >A Web-based Tool for Semi-interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities
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A Web-based Tool for Semi-interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities

机译：基于Web的基于网络的工具，用于半交互性核型型染色体图像分析染色体异常

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Chromosome abnormalities relate to several genetic diseases. These abnormalities can be diagnosed based on the analysis of karyogram of the human chromosomes. However, the manual chromosome karyotyping process is often time consuming. This paper presents a novel web-based tool, Biochrom, to assist the cytogeneticist in producing the karyogram. Biochrom is a semi-automated tool, which provides both manual and automated functions in chromosomes segmentation and classification using image processing combined with machine learning techniques. The study is carried on 612 metaphase images with 48 of those containing abnormal chromosomes. We compare the proposed tool to a conventional public tool, Metasel, for karyotyping based on performance by two cytogeneticists using user experience metrics such as number of manual interactions and processing time. Moreover, we quantitatively evaluate the accuracy of the classification of two approaches: Support Vector Machine (SVM) and deep learning. The evaluation results show that the deep learning classification outperforms SVM classification, and our proposed tool requires fewer interactions and less time consuming to complete the karyotyping task on average.
机译：染色体异常涉及几种遗传疾病。可以基于人染色体的核心标准的分析来诊断这些异常。但是，手动染色体核型化过程通常是耗时的。本文介绍了一种新型的基于Web的工具，Biochrom，以帮助细胞发生器产生核心线。 Biochrom是一个半自动工具，它在染色体分割和分类中提供了手动和自动功能，使用图像处理与机器学习技术相结合。该研究载于612个中期图像，其中48种含有异常染色体。我们将所提出的工具与传统的公共工具，MetaSel进行比较，用于基于两个细胞遗传学家的性能使用用户体验度量，例如手动交互和处理时间。此外，我们定量评估了两种方法分类的准确性：支持向量机（SVM）和深度学习。评估结果表明，深度学习分类优于SVM分类，我们所提出的工具需要更少的互动，并且耗时耗时，以平均完成核型特写任务。

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