Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant.Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient’s lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI).Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0–3 days), progressive and peak stage (4–16 days), and absorption stage (17–42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17–30] vs. 39 [31–44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time.Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

Aiming at the problems of long time-consuming and low accuracy in extracting buildings with traditional machine learning methods. In this paper, the SegNet semantic segmentation models on deep learning is used to improve the algorithm, and a high-resolution remote sensing image building extraction algorithm based on sparse constrained SegNet are proposed. First, regular terms and Dropout are added to the SegNet model, which greatly reduces the occurrence of model over-fitting; secondly, in order to extract richer semantic features for the model, the algorithm introduces a pyramid pooling module; finally, the Lorentz function sparse constraint factor is introduced to the SPNet model, Construct a new semantic segmentation model LSPNet. In order to verify the reliability and applicability of the proposed algorithm, the optimized LSPNet model is used to identify and extract the buildings in the high-resolution data set. Experimental results show that compared with traditional machine learning methods, this method has the advantages of fast convergence and high accuracy, and has a good application prospect.