利用人工智能技术在x射线图像中的肺炎分类

Han Trong Thanh, P. H. Yen, Trinh Bich Ngoc
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引用次数: 2

摘要

本文重点研究图像分类算法,即图像显示细菌和病毒引起的肺炎的病理。该方法基于VGG16、VGG19、DenseNet169网络提取数据特征并进行模型分类训练。x光片分为正常人、病毒性肺炎和细菌性肺炎。提供的来源是由专家手动分类的患者胸部X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射线图像是否被正确分类。在本研究中,这些算法给出了相对积极的分类结果,准确率约为85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology
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%.
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