ct images segmentation of lungs with covid-19 infection using mask r-cnn

The coronavirus (covid-2019) pandemic has caused a catastrophic effect on health and global economy. the most common standard for confirming the virus relies on rt-pcr tests. as a complement to rt-pcr, computed tomography (ct) can be used for diagnosing covid-19. we describe the r-cnn (area-based torsional neural network)approach to segmentation of ct images of the lungs of people with covid-19 using a variety of augmentation methods. the class imbalance problem leads to inefficient training, which makes model degenerated. in this paper, we have used a method based on mask r-cnn to segment left lung, right lung, covid-19 infection. in our model, the focal loss function is used to suppress well-classified examples. the model is tested on covid-19-ct-seg-20cases dataset and the results showed that the accuracy reaches 87.93%. compared with the smooth loss function in mask r-cnn it improves by 5%. therefore, this model will aid health professionals to fasten the screening and validation of the initial assessment towards covid-19 patients.