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Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology

机译:使用人工智能技术的X射线图像中的肺炎分类

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The article focuses on the research of image classification algorithms, namely the images indicate pathology of pneumonia caused by bacteria and viruses. The proposed method is based on using the VGG16, VGG19, DenseNet169 networks to extract data characteristics and train the model classification. The X-rays are classified including normal people, patients with viral pneumonia, and bacterial pneumonia. The provided source was medical data on chest X- ray images of patients who were manually classified by specialists. However, the accuracy of the classification is highly dependent on the number of images, the resolution of the images, and whether the X-ray image is correctly classified. In this study, the algorithms give relatively positive classification results with an accuracy of approximately 85%.
机译:本文侧重于图像分类算法的研究,即图像表明由细菌和病毒引起的肺炎的病理学。 所提出的方法是基于使用VGG16,VGG19,DenSenet169网络来提取数据特征并培训模型分类。 X射线分类,包括正常人,病毒性肺炎和细菌肺炎。 提供的来源是由专家手动分类的患者的胸部X射线图像的医学数据。 然而,分类的准确性高度依赖于图像的数量,图像的分辨率以及X射线图像是否正确归类。 在这项研究中,算法给出了相对阳性的分类结果,精度约为85%。

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