Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.
Evaluation of Normalization and PCA on the Performance of Classifiers for Protein Crystallization Images.
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DOI：
10.1109/secon.2014.6950744
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
Aygün RS
中科院分区：
文献类型：
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作者：
Dinç İ;Sigdel M;Dinç S;Sigdel MS;Pusey ML;Aygün RS
在本文中，我们研究了蛋白质晶体生长过程中捕获的蛋白质结晶图像的分类性能。我们将蛋白质结晶图像分为3类：非晶体，可能的铅（可能产生晶体形成的条件）和晶体。在这项研究中，我们只考虑非晶体和可能导致蛋白质结晶图像分开的子类。我们使用5种不同的分类器来解决这个问题，并将一些数据预处理方法，如主成分分析（PCA），最小-最大（MM）归一化和z分数（ZS）归一化方法应用于我们的数据集，以评估它们对非晶体和可能的导联数据集分类器的影响。我们对1606个非晶体图像和245个可能的引线图像独立进行了实验。我们对这两个数据集都有满意的结果。我们对非晶体数据集的准确率达到96.8%，对可能的导联数据集的准确率达到94.8%。我们的目标是调查最好的分类器与最佳的预处理技术在非晶体和可能的铅数据集。
In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of noncrystal and likely leads protein crystallization images separately. We use 5 different classifiers to solve this problem and we applied some data preprocessing methods such as principal component analysis (PCA), min-max (MM) normalization and z-score (ZS) normalization methods to our datasets in order to evaluate their effects on classifiers for the noncrystal and likely leads datasets. We performed our experiments on 1606 noncrystal and 245 likely leads images independently. We had satisfactory results for both datasets. We reached 96.8% accuracy for noncrystal dataset and 94.8% accuracy for likely leads dataset. Our target is to investigate the best classifiers with optimal preprocessing techniques on both noncrystal and likely leads datasets.