Imagine you have a super smart doctor who can quickly and accurately diagnose severe pulmonary infections just by looking at computed tomography (CT) images. Well, researchers have developed a deep convolutional neural network (CNN) called EC-U-net that does just that! They trained the EC-U-net model using CT images from 120 patients with severe pneumonia and pulmonary infection. The results were impressive, with the EC-U-net model outperforming traditional CNN algorithms in terms of accuracy and ability to detect different types of infection-related abnormalities. The model even had lower false negative and false positive rates! This means that the EC-U-net model can help doctors identify pulmonary infection lesions more accurately, leading to improved diagnosis and timely treatment. If you’re interested in learning more about this groundbreaking research, check out the link below!

This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment.

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