Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images.
Pre-Trained Deep Convolutional Neural Network for Clostridioides Difficile Bacteria Cytotoxicity Classification Based on Fluorescence Images.
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DOI：
10.3390/s20236713
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发表时间：
2020-11-24
期刊：
影响因子：
--
通讯作者：
Bogyo M
中科院分区：
文献类型：
--
作者：
Brodzicki A;Jaworek-Korjakowska J;Kleczek P;Garland M;Bogyo M
艰难梭菌感染（CDI）是一种肠道细菌性疾病，在世界范围内的发病率正在增加。CDI的症状范围从轻度腹泻到严重危及生命的结肠炎症。虽然抗生素是CDI的标准治疗方法，但它们也是CDI发展和复发的最大风险因素。因此，成功治疗CDI并防止复发的新疗法是未满足的临床需求。新药先导物的筛选通常通过手动图像分析进行测试。这一过程缓慢、乏味，而且容易出现人为错误和偏见。到目前为止，很少有工作集中在基于荧光图像的计算机辅助筛选药物线索。在这里，我们提出了一种新的方法来识别暴露于C的人成纤维细胞的特征性形态学变化。基于深度学习方法支持的计算机视觉算法的艰难毒素。用于预处理阶段的经典图像处理算法与负责细胞分类的经调整的预训练深度卷积神经网络一起使用。在这项研究中，我们利用迁移学习方法，通过检查预训练的VGG-19，ResNet 50，Xception和DenseNet 121卷积神经网络（CNN）模型以及调整后的密集连接分类器。我们将获得的结果与其他机器学习算法的结果进行比较，并对其进行可视化和解释。所提出的模型已被评估的数据集包含369个图像与6112例。DenseNet 121的准确性、灵敏度和特异性分别为93.5%、92%和95%。
Clostridioides difficile infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence. Therefore, novel therapies that successfully treat CDI and protect against recurrence are an unmet clinical need. Screening for novel drug leads is often tested by manual image analysis. The process is slow, tedious and is subject to human error and bias. So far, little work has focused on computer-aided screening for drug leads based on fluorescence images. Here, we propose a novel method to identify characteristic morphological changes in human fibroblast cells exposed to C. difficile toxins based on computer vision algorithms supported by deep learning methods. Classical image processing algorithms for the pre-processing stage are used together with an adjusted pre-trained deep convolutional neural network responsible for cell classification. In this study, we take advantage of transfer learning methodology by examining pre-trained VGG-19, ResNet50, Xception, and DenseNet121 convolutional neural network (CNN) models with adjusted, densely connected classifiers. We compare the obtained results with those of other machine learning algorithms and also visualize and interpret them. The proposed models have been evaluated on a dataset containing 369 images with 6112 cases. DenseNet121 achieved the highest results with a 93.5% accuracy, 92% sensitivity, and 95% specificity, respectively.
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影响因子：
4.2
作者：
Li, Benjamin Y.;Oh, Jeeheh;Wiens, Jenna
通讯作者：
Wiens, Jenna
影响因子：
8.1
作者：
Johnson, Justin M.;Khoshgoftaar, Taghi M.
通讯作者：
Khoshgoftaar, Taghi M.
影响因子：
2.7
作者：
Garland, Megan;Jaworek-Korjakowska, Joanna;Sienczyk, Marcin
通讯作者：
Sienczyk, Marcin
影响因子：
3.4
作者：
Hughes D;Karlén A
通讯作者：
Karlén A
DOI：
10.1093/cid/cis319
发表时间：
2012-08
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Lessa FC;Gould CV;McDonald LC
通讯作者：
McDonald LC