COVID 19 Severity of Pneumonia Analysis Using Chest X Rays

Narayana Darapaneni, Shweta Ranjane, Uday Shankar Pallavajula Satya, D.Krishna prashanth, M. Reddy, A. Paduri, Aravind Kumar Adhi, Vachaspathi Madabhushanam
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引用次数: 7

Abstract

Purpose: To identify pneumonia location and determine the severity of pneumonia using deep learning network on chest X-ray images Methods: Data from RSNA Pneumonia detection challenge [1] from Kaggle is used for train and test analysis. Identifying images and calculating severity percentage of lung opacity in pneumonia present images by drawing bounding box Results: With 4668 X-ray images trained and tested on 1500 X-ray images, initial model has shown a mean average precision (mAP) of 0.90 on train set and 0.89 on test set. Conclusion: The intention is to leverage on existing studies and develop a better performing and highly accurate deep learning model to calculate severity percentage in a pneumonia present chest x-ray image.
利用胸部X线分析COVID - 19肺炎严重程度
目的:利用深度学习网络对胸部x线图像进行肺炎位置识别和肺炎严重程度判断方法:使用Kaggle的RSNA肺炎检测挑战[1]数据进行训练和测试分析。结果:对4668张x射线图像进行训练,在1500张x射线图像上进行测试,初始模型在训练集上的平均精度(mAP)为0.90,在测试集上的平均精度(mAP)为0.89。结论:目的是利用现有的研究,开发一个性能更好、高度准确的深度学习模型,以计算肺炎胸部x线图像的严重程度百分比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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