FIGURE 2.

Illustration of the machine learning (ML) Model and deep learning (DL) Models. (a) Classic machine learning models (CMLM) were trained with radiomics features or the combination of clinical and radiomics features to differentiate COVID‐19 and community acquired pneumonia (CAP), including k‐nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR). (b) An improved three dimensional convolutional neural network (3D CNN) model (3DCM), which constituted three convolutional blocks of Resnet and three fully connected layers, was employed to distinguish between COVID‐19 and CAP using CT images with or without the addition of clinical information. (c) A novel algorithm based on multi‐instance learning and long and short‐term memory (LSTM) (3DMTM) was proposed for COVID‐19 identification. Lesion instance generator enabled efficient selection of instance (slices) with lesions; feature instance generator based on Resnet‐18 extracted features from input instances. Clinical information could be concatenated after feature extraction. Long and short‐term memory (LSTM) helped obtain the spatial information by combining features from different layers