Figure 2 Compared with mainstream deep learning methods such as Deep-COVID, the AANet network has greatly improved the accuracy of disease location, and the color depth of the saliency map and the matching degree of the location of the disease have been significantly improved
Funded by the National Natural Science Foundation of China (approval numbers: 61973087, 61773127, 62073086), Guangdong University of Technology's intelligent information processing team Xie Shengli, He Zhaoshui, Lu Jun, etc.
Since 2020, new coronary pneumonia has ravaged the world, causing huge losses and triggering a worldwide health crisis (World Health Organization statistics point out that as of October 19, 2021, a total of 240,940,937 cases of new coronary pneumonia have been diagnosed globally and 4,903,911 deaths)
In traditional imaging diagnosis, doctors need to consult the images one by one based on their own experience.
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