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Proceedings Paper

Pneumonia detection based on deep neural network Retinanet
Author(s): Mao Liu; Yumeng Tan; Lina Chen
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Paper Abstract

The interpretation of chest x-rays is critical for the discovery of thoracic diseases, including pneumonia and lung cancer, which affect millions of people worldwide each year. This time-consuming task usually requires radiologists to read the images, leading to diagnostic errors due to fatigue and lack of diagnostic expertise in areas where there are no radiologists in the world. Recently, deep learning methods have been able to perform well in the field of medical imaging, thanks to the emergence of large network architectures and large labeled datasets. In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only open-source deep learning object detection and is based on RetinaNet, a fully convolutional network which incorporated global and local features for object detection. Our method achieves the classification and localization of Chest radiograph pneumonia by key modifications to the image preprocessing and training process, and incorporates bounding boxes from multiple models during the test. Improve the effect of algorithm classification and localization. After image enhancement and algorithm improvement, we randomly selected 100 chest radiographs on the second stage chest dataset to test our detection algorithm and achieved good results. Our findings yield an accuracy of 90.25%.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210F (27 November 2019); doi: 10.1117/12.2539633
Show Author Affiliations
Mao Liu, Zhejiang Normal Univ. (China)
Yumeng Tan, Zhejiang Normal Univ. (China)
Lina Chen, Zhejiang Normal Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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