Objectives An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. Methods In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. Results The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/ 336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). Conclusions A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. Key Points • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations. Keywords Computer-assisted diagnosis. Volume CT. COVID-19. Artificial intelligence. Deep learning Abbreviations AUC Area under the curve CAD Computer-assisted diagnosis CAP Community-acquired pneumonia COVID-19 Coronavirus disease 2019 IgG Immunoglobulin G IgM Immunoglobulin M ROC Receiver operating characteristic RT-PCR Reverse transcription-polymerase chain reaction SSAC Sparse separable atrous convolution Jin-Cao Yao and Tao Wang contributed equally to this work.
Plant disease is one of the major factors threatening the plant growth. In this paper, we utilize the region proposed network (RPN) to detect and locate the plant leaf based on the machine deep learning algorithm. Firstly, the original image needs to be input into convolution neural network (CNN). After several convolution and pooling operations, highly condensed image features can be obtained. Secondly, a reference boundary frame for predicting the position of an object can be obtained by sliding nine boundary frames as sliding windows on the feature map. Two neural networks are input into each boundary box to get the classification result and boundary location. Finally, with the help of non-maximum suppression algorithm (NMS), multiple boundary boxes for the same object are eliminated and only the best boundary boxes are retained. Experiments show that RPN algorithm has better performance on locating the diseased leaves in complex environment, thus reducing the influence of disease on agricultural production. At the same time, it is of great significance in economic development, ecological protection, agricultural production and other fields.
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