Reconstruction Algorithm-Based Computed Tomography Image Feature for Evaluating the Effect of Internal Administration and Medicated Bath of Liangxue Xiaoyin Decoction on Psoriasis Vulgaris.

Skin computed tomography (CT) image based on improved marching cubes (MC) algorithm was explored to evaluate the therapeutic effect of internal administration of Liangxue Xiaoyin decoction combined with medicated bath in the treatment of psoriasis vulgaris. 712 patients with psoriasis vulgaris blood heat syndrome in hospital were recruited as the research object, which were randomly divided into observation group (TCM oral therapy combined with medicinal bath) and control group (TCM oral therapy), each with 356 cases. Psoriasis area and severity index (PASI), pruritus degree, and clinical treatment effect were compared. The results showed that the reconstruction time of median method was greatly shorter, and the algorithm efficiency was improved by 40.6290%. After treatment, the psoriasis area and severity index (PASI) score of the observation group was 5.61 ± 1.15, ΔPASI = (22.64 ± 2.15). ΔPASI% = 80.14%, which were greatly higher than the control group ((9.41 + 1.56) points, ΔPASI = (18.84 + 1.65) points, ΔPASI% = 66.69%) (P < 0.05). After treatment, the itching degree of the observation group was 3.03 ± 1.01 points, which was lower than that of the control group ((3.71 ± 1.06) points), and the itching degree of the observation group was greater than that of the control group, with substantial difference (P < 0.05). The total effective rate of observation group (88.76%) was higher than that of control group (71.07%) (P < 0.05). Therefore, skin CT image based on the improved MC algorithm can evaluate the therapeutic effect of internal administration of Liangxue Xiaoyin decoction combined with medicated bath in the treatment of psoriasis vulgaris. The internal administration of Liangxue Xiaoyin decoction combined with medicated bath had a good effect on the treatment of psoriasis vulgaris and was of certain clinical application value.

Judgement of the Clinical Value of Spiral CT Three-Dimensional Reconstruction in the Diagnosis of Lumbar Degenerative Osteoporosis.

With the aging of the population, there are more and more degenerative diseases of the lumbar spine that accompany osteoporosis. Lumbar degenerative osteoporosis has also become fragile and high in incidence, which has also attracted the attention of experts and scientists in related fields. Degeneration of the lumbar spine often causes pain in the waist and surrounding patients and even affects their life safety. The lesions such as the shoulders and lower back often show varying degrees of softening or induration in the fracture line or osteoporosis will directly produce adverse reactions to joint activities and then cause the development and deterioration of various complications. At present, spiral CT three-dimensional reconstruction technology has been widely used in the field of medical imaging and has played a very important role in the diagnosis and treatment of some diseases. Therefore, combined with three-dimensional reconstruction of spiral CT, this paper discusses its clinical value in the diagnosis of lumbar degenerative osteoporosis. In this experiment, in order to understand the image results after three-dimensional reconstruction, five groups of cases were selected for testing. The test items include the whole lesion site, vertebral imaging, soft tissue lesion site, and lumbar lesion site. In addition, in order to understand the clinical value of spiral CT three-dimensional reconstruction in the diagnosis of lumbar degenerative osteoporosis, this technique was compared and tested with other imaging methods. The selected imaging methods include X-ray, CT, and MRI. The test items include sensitivity, accuracy, positive predictive value, and negative predictive value. To explore the clinical value of spiral CT three-dimensional reconstruction in the diagnosis of lumbar degenerative osteoporosis, from the experimental results, the relevant image clarity and accuracy of the five groups of cases are high, the image quality after three-dimensional reconstruction is good, and the clarity and accuracy are high. In addition, the sensitivity and accuracy of spiral CT three-dimensional reconstruction are higher than those of other imaging methods. It has great clinical value in the diagnosis and treatment of lumbar degenerative osteoporosis.

Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images.

Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with three different types of skip connections. The proposed connections work to recover the loss of high-level features on the convolutional path of the first U-Net due to the pooling and the loss of low-level features during the upsampling path of the first U-Net. The skip connections concatenate all the features that are generated at the same level from the previous paths to the inputs of the convolutional layers in both paths of the second U-Net in a densely connected manner. We implement two versions of the model with different number of filters at each level of each U-Net by maximising the Dice similarity between the predicted liver region and that of the ground truth. The proposed models were trained with 3Dircadb public dataset that were released for Sliver and 3D liver and tumour segmentation challenges during MICCAI 2007-2008 challenge. The experimental results show that the proposed model outperformed the original U-Net and 2-U-Nets variants, and is comparable to the state-of-the-art mU-Net, DC U-Net, and Cascaded UNET.

Three-Dimensional Skin CT Based on Intelligent Algorithm in the Analysis of Skin Lesion Sites Features in Children with Psoriasis.

This research was to explore the application value of three-dimensional computed tomography (CT) based on artificial intelligent algorithm in analyzing the characteristics of skin lesions in children with psoriasis. In this study, 15 children with psoriasis were selected as the observation group, and 15 children with other skin diseases were selected as the control group. The CT images were optimized, and the feature selection was carried out based on artificial intelligent algorithm. Firstly, the results were compared with the results of simple skin three-dimensional CT to determine the effectiveness. Then, the two groups of three-dimensional skin CT image features of skin psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium based on intelligent algorithms were compared. After comparison, the detection rate of psoriasis-like hyperplasia, Munro microabscess, dermal papillary vascular dilation, and squamous epithelium in the observation group was higher than that in the control group, with significant difference and statistical significance (P < 0.05). In addition, the sensitivity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 80.0%, 86.7%, 80.0%, and 93.3%, respectively. The specificity of psoriasis-like hyperplasia, Munro microabscess, dermal papilla vascular dilatation, and squamous epithelium in children with psoriasis was 86.7%, 93.3%, 60.0%, and 73.3%, respectively. The results showed that Munro microabscess and psoriasis-like hyperplasia had high sensitivity and specificity in all diagnostic items, which could be used as important features of skin lesion sites in the diagnosis of psoriasis in children. The research provides a basis for the clinical diagnosis of psoriasis in children, which is worthy of clinical promotion.

Intelligent Algorithm-Based Coronary Angiography Characteristics of Acute Non-ST-Segment Elevation Myocardial Infarction Patients with Different Genders.

OBJECTIVE: This study was aimed at comparing the characteristics of coronary angiography based on intelligent algorithm in patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) of different genders. METHODS: Eighty patients were selected to segment the coronary angiogram using the convolutional neural network (CNN) algorithm, the input layer of the CNN was used to receive the image dataset, and three-dimensional data were input during semantic segmentation to achieve automatic segmentation of the target features. Segmentation results were quantitatively assessed by accuracy (Acc), sensitivity (Se), specificity (Sp), and Dice coefficient (Dice). The characteristics of coronary angiography were compared between the two groups. RESULTS: The CNN algorithm had good segmentation effect, complete vessel extraction, and little noise, and Acc, Se, Sp, and Dice were 90.32%, 93.39%, 91.25%, and 89.75%, respectively. The proportion of diabetes mellitus was higher in female patients with NSTEMI (68.8%) than that in male patients (46.3%); the proportion of the left main coronary artery (LM) and left anterior descending artery (LAD) was lower in the female group (7.5%, 41.3%) than that in the male group (13.8%, 81.3%), and the difference between the two groups was statistically significant (P < 0.05). CONCLUSION: The CNN algorithm achieves accurate extraction of vessels from coronary angiographic images, and women with diabetes and hyperlipidemia are more likely to have NSTEMI than men, especially the elderly.

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules.

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer.

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

Intelligent Algorithm-Based Multislice Spiral Computed Tomography to Diagnose Coronary Heart Disease.

In this study, dictionary learning and expectation maximization reconstruction (DLEM) was combined to denoise 64-slice spiral CT images, and results of coronary angiography (CAG) were used as standard to evaluate its clinical value in diagnosing coronary artery diseases. 120 patients with coronary heart disease (CHD) confirmed by CAG examination were retrospectively selected as the research subjects. According to the random number table method, the patients were divided into two groups: the control group was diagnosed by conventional 64-slice spiral CT images, and the observation group was diagnosed by 64-slice spiral CT images based on the DLEM algorithm, with 60 cases in both groups. With CAG examination results as the standard, the diagnostic effects of the two CT examination methods were compared. The results showed that when the number of iterations of maximum likelihood expectation maximization (MLEM) algorithm reached 50, the root mean square error (RMSE) and peak signal to noise ratio (PSNR) values were similar to the results obtained by the DLEM algorithm under a number of iterations of 10 when the RMSE and PSNR values were 18.9121 dB and 74.9911 dB, respectively. In the observation group, 28.33% (17/60) images were of grade 4 or above before processing; after processing, it was 70% (42/60), significantly higher than the proportion of high image quality before processing. The overall diagnostic consistency, sensitivity, specificity, and accuracy (88.33%, 86.67%, 80%, and 85%) of the observation group were better than those in the control group (60.46%, 62.5%, 58.33%, and 61.66%). In conclusion, the DLEM algorithm has good denoising effect on 64-slice spiral CT images, which significantly improves the accuracy in the diagnosis of coronary artery stenosis and has good clinical diagnostic value and is worth promoting.

Multislice Spiral Computed Tomography Postprocessing Technology in the Imaging Diagnosis of Extremities and Joints.

OBJECTIVE: To improve the clinical detection rate of bone and joint fractures of the extremities and to explore the value and significance of the application of multislice spiral computed tomography (MSCT) postprocessing technology in diagnosis. METHODS: 80 patients with bone and joint fractures of the extremities admitted to the hospital were selected as the research objects. The patients received X-ray digital radiography (DR) plain film examination and then MSCT examination. At the same time, multiplane reconstruction (MPR) and surface shadow display (SSD) and volume rendering three-dimensional imaging (VRT) technology and other postprocessing technologies compare the differences in the detection rate of limbs and joint fractures between the two inspection methods. RESULTS: A total of 100 fractures were found in 80 patients. The detection rate of X-ray DR was 69%. After MSCT postprocessing technology, the detection rates of MPR, SSD, and VRT were 96%, 98%, and 99%, respectively. The accuracy of MSCT postprocessing technology in diagnosing extremity bone and joint fractures was significantly higher than that of DR, and the difference between groups was statistically significant. CONCLUSION: MSCT postprocessing technology for patients with extremity bone and joint fractures has a good effect. It is not only noninvasive but also has a high detection rate. It can significantly reduce the missed and misdiagnosed rate and provide detailed imaging data for the formulation of clinical treatment plans.

X-Ray Film under Artificial Intelligence Algorithm in the Evaluation for Nursing Effect of Gamma Nail Internal Fixation in Elderly Patients with Intertrochanteric Fracture of Femur.

The aim of this work was to explore the effects of Gamma nail internal fixation for intertrochanteric fracture of femur by X-ray film classification and recognition method based on artificial intelligence algorithm. The study subjects were 100 elderly patients with intertrochanteric fracture of femur admitted to hospital. The cases were diagnosed as elderly (over 60 years old) femoral intertrochanteric fractures by X-ray or CT. They were divided into two groups, with 50 persons in each group: one group used the X-ray film evaluation image guidance based on the artificial intelligence algorithm (research group), and the other group did not use algorithmic guidance (control group). The results showed that the segmentation effect of the proposed algorithm was similar to the gold standard segmentation result, indicating that the algorithm was effective and feasible in the segmentation of fractures and bones. The global level set algorithm was set as the control. The ultimate measurement accuracy (UMA) value of the algorithm group was (1.77 ± 0.22), and the UMA value of the global level set algorithm group was (3.42 ± 0.36), indicating that the image processed by the algorithm group had obvious numerical effect, high accuracy, and good retention of details. The operation time, intraoperative blood loss, incision length, hospital stay, weight-bearing time, and fracture healing time of the two groups were all better than those of the control group. One month after surgery, the Harris score of the algorithm group was 67, and that of the control group was 51, with a 16-point difference between the two groups (p < 0.05). The patient had less pain and fast recovery speed, indicating that it was a good way to treat elderly intertrochanteric fractures with the nursing effect of X-ray Gamma nail internal fixation based on an artificial intelligence algorithm. The artificial intelligence algorithm not only can be applied to the Gamma nail internal fixation of elderly patients with intertrochanteric fractures but also can be applied to the X-ray image processing of other fractures and other surgical methods to provide effective treatment for fracture patients.

RDFNet: A Fast Caries Detection Method Incorporating Transformer Mechanism.

Dental caries is a prevalent disease of the human oral cavity. Given the lack of research on digital images for caries detection, we construct a caries detection dataset based on the caries images annotated by professional dentists and propose RDFNet, a fast caries detection method for the requirement of detecting caries on portable devices. The method incorporates the transformer mechanism in the backbone network for feature extraction, which improves the accuracy of caries detection and uses the FReLU activation function for activating visual-spatial information to improve the speed of caries detection. The experimental results on the image dataset constructed in this study show that the accuracy and speed of the method for caries detection are improved compared with the existing methods, achieving a good balance in accuracy and speed of caries detection, which can be applied to smart portable devices to facilitate human dental health management.

CT Image Features of the FBP Reconstruction Algorithm in the Evaluation of Fasting Blood Sugar Level of Diabetic Pulmonary Tuberculosis Patients and Early Diet Nursing.

The study was aimed at exploring the application value of the CT image based on a filtered back projection (FBP) algorithm in the diagnosis of patients with diabetes complicated with tuberculosis and at analyzing the influence of dietary nursing on patients with diabetes complicated with tuberculosis. In this study, the FBP algorithm was used to optimize CT images to effectively obtain reconstructed ROI images. Then, the deviation from measurement values of reconstructed images at different pixel levels was analyzed. 138 patients with diabetes complicated with tuberculosis were selected as research subjects to compare the number of lung segments involved and the CT imaging manifestations at different fasting glucose levels. All patients were divided into the control group (routine drug treatment) and observation group (diet intervention on the basis of drug treatment) by random number table method, and the effect of different nursing methods on the improvement of patients' clinical symptoms was discussed. The results showed that the distance measurement value decreased with the increase in pixel level, there was no significant difference in the number of lung segments involved in patients with different fasting glucose levels (P > 0.05), and there were statistically significant differences in the incidence of segmental lobar shadow, bronchial air sign, wall-less cavity, thick-walled cavity, pulmonary multiple cavity, and bronchial tuberculosis in patients with different fasting glucose levels (P < 0.05). Compared with the control group, 2 h postprandial blood glucose level in the observation group was significantly improved (P < 0.05), there was a statistical significance in the number with reduced pleural effusion and the number with reduced tuberculosis foci in the two groups (P < 0.05), and the level of hemoglobin in the observation group was 7.1 ± 1.26, significantly lower than that in the control group (8.91 ± 2.03, P < 0.05). It suggested that the changes of CT images based on the FBP reconstruction algorithm were correlated with fasting blood glucose level. Personalized diet nursing intervention can improve the clinical symptoms of patients, which provides a reference for the clinical diagnosis and treatment of patients with diabetes complicated with tuberculosis.

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features.

OBJECTIVES: To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. MATERIALS AND METHODS: In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. RESULTS: Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. CONCLUSIONS: Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.

Segmentation and Automatic Identification of Vasculature in Coronary Angiograms.

Coronary angiography is the "gold standard" for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method.

An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks.

There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k-means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f-score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.

Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function.

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

A Segmentation Method of Foramen Ovale Based on Multiatlas.

Trigeminal neuralgia is a neurological disease. It is often treated by puncturing the trigeminal nerve through the skin and the oval foramen of the skull to selectively destroy the pain nerve. The process of puncture operation is difficult because the morphology of the foramen ovale in the skull base is varied and the surrounding anatomical structure is complex. Computer-aided puncture guidance technology is extremely valuable for the treatment of trigeminal neuralgia. Computer-aided guidance can help doctors determine the puncture target by accurately locating the foramen ovale in the skull base. Foramen ovale segmentation is a prerequisite for locating but is a tedious and error-prone task if done manually. In this paper, we present an image segmentation solution based on the multiatlas method that automatically segments the foramen ovale. We developed a data set of 30 CT scans containing 20 foramen ovale atlas and 10 CT scans for testing. Our approach can perform foramen ovale segmentation in puncture operation scenarios based solely on limited data. We propose to utilize this method as an enabler in clinical work.

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis.

Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.

CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network.

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.