利用胸部X线分析COVID - 19肺炎严重程度

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI:10.1109/ICIIS51140.2020.9342702

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

摘要

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。

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COVID 19 Severity of Pneumonia Analysis Using Chest X Rays

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.

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2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)

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