Potential risk of X-ray radiation from computed tomography (CT) has been a concern of the public. However, simply decreasing the dose will degrade quality of the CT images and compromise diagnostic performance. In this paper, we propose a noise learning generative adversarial network coupling with least squares, structural similarity and L1 losses for low-dose CT denoising. In our method, noise distributed in the input low-dose CT image is learned by the generator network and then subtracted from the input to generate the final denoised version. The denoised CT images are penalized by the least squares loss function, and they are pulled toward boundary of the decision even though they are classified as normal-dose CT. Least squares stabilize the training process without regularization. Structural similarity and L1 losses are utilized to keep textural details and sharpness of the denoised CT images respectively. Experiments and results show that our method can effectively suppress noise and remove artifacts compared with the state-ofthe-art methods. The texture statistical properties, which include mean, standard deviation, uniformity, and entropy, further confirm that the generated noise-reduced CT image is as close as to that of the normal-dose counterpart. INDEX TERMS Deep learning, generative adversarial network, least squares, low-dose CT, denoising.

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Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression, i.e., from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, and F1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.