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2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)最新文献

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Using Multi-Feature Selection with machine learning for De novo Acute Myeloid Leukemia in Egypt 多特征选择与机器学习在埃及新生急性髓性白血病中的应用
P. El-Kafrawy, Ibrahim I. M. Manhrawy, Hanaa Fathi, Mohammed Qaraad, A. Kelany
De novo Acute Myeloid Leukemia is one of the diseases from which many people die each year. It is the most common type of all types of cancer and causes death of people all over the world. Classification methods are an efficient means to separate data. Especially in the field of medicine, where these methods are widely used in diagnosis and analysis for decision-making. In this paper, we consider group feature selection in a multiclass classification of other ways. The performance will be compared between different machine learning algorithms: Random Forest classifier (RF), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB) on AML dataset National Cancer Institute (NCI), Cairo University. The main objective is to evaluate the correction in the classification of the data concerning the efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity, and specificity. Experimental results determine that LR gives the enormous accuracy (92.30%) with the lowest error rate. All experiments are affected within a simulation environment and manipulated in Python 3.7 data mining tool.
新生急性髓系白血病是每年导致许多人死亡的疾病之一。它是所有类型的癌症中最常见的一种,导致世界各地的人死亡。分类方法是分离数据的有效手段。特别是在医学领域,这些方法被广泛应用于诊断和决策分析。在本文中,我们考虑了组特征选择在多类分类中的其他方法。将在AML数据集上比较不同机器学习算法的性能:随机森林分类器(RF)、逻辑回归(LR)、决策树(DT)、支持向量机(SVM)和朴素贝叶斯(NB)。主要目的是评估数据分类中的校正,涉及到每个算法在准确性、精密度、灵敏度和特异性方面的效率和有效性。实验结果表明,LR具有很高的准确率(92.30%)和最低的错误率。所有实验都在模拟环境中受到影响,并在Python 3.7数据挖掘工具中进行操作。
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引用次数: 4
Brain Tumor Segmentation Based on Deep Learning 基于深度学习的脑肿瘤分割
Hajar Cherguif, J. Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, H. Tairi
Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. Determining the extent of the tumor is a major challenge in brain tumor treatment planning and quantitative assessment to ameliorate the quality of life of patients. Magnetic resonance imaging (MRI) is an imaging technique widely used to evaluate these brain tumors, but manual segmentation prevented by the large amount of data generated by the MRI is a very long task and the performance is highly dependent on operator's experience. In this context, a reliable automatic segmentation method for segmenting the brain tumor is necessary for effective measurement of the extent of the tumor. There are several image segmentation algorithms, each with its own advantages and limitations. In this paper, we propose a method based on Deep Learning, using deep convolution networks based on the U-Net model. Our method was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2017 datasets, which contain both HGG and LGG patients. Based on the experiments, our method can provide a segmentation that is both efficient and robust compared to the manually delineated ground truth. Our model showed a maximum Dice Similarity Coefficient metric of 0.81805 and 0.8103 for the dataset used.
脑肿瘤发展迅速,具有侵袭性,会造成脑损伤,并可能危及生命。确定肿瘤的范围是脑肿瘤治疗计划和定量评估以改善患者生活质量的主要挑战。磁共振成像(MRI)是一种广泛用于评估这些脑肿瘤的成像技术,但由于MRI产生的大量数据阻止了人工分割,这是一项非常耗时的任务,并且性能高度依赖于操作员的经验。在这种情况下,需要一种可靠的自动分割脑肿瘤的方法来有效地测量肿瘤的范围。有几种图像分割算法,每种算法都有自己的优点和局限性。在本文中,我们提出了一种基于深度学习的方法,使用基于U-Net模型的深度卷积网络。我们的方法在医学图像计算和计算机辅助干预BRATS 2017数据集提供的真实图像上进行了评估,其中包括HGG和LGG患者。实验结果表明,该方法与人工分割的地面真值相比,具有高效和鲁棒性。我们的模型显示所使用数据集的最大Dice Similarity Coefficient度量为0.81805和0.8103。
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引用次数: 10
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2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)
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