Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease.

This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithms on patients with diabetic kidney disease (DKD). 44 patients diagnosed with DKD were randomly divided into two groups: group A (no nursing intervention) and group B (comprehensive nursing). In the same period, 32 healthy volunteers were selected as the control group. Ultrasonographic images based on the K non-local-means (KNL-Means) filtering algorithm were used to perform imaging examinations in healthy people and DKD patients before and after care. The results suggested that compared with those of the SAE reconstruction algorithm and KAVD reconstruction algorithm, the PSNR value of artificial bee colony algorithm reconstruction of image was higher and the MSE value was lower. The resistant index (RI) of DKD patients in group B after nursing was 0.63 ± 0.06, apparently distinct from the RI of the healthy people (controls) in the same group (0.58 ± 0.06) and the RI of DKD patients in group A (0.68 ± 0.07) (P < 0.05). The incidence rate of complications in DKD patients in group B was apparently inferior to that in group A. After comprehensive nursing intervention (CNI), the scores of all dimensions of quality of life (QoL) in DKD patients in group B were obviously superior versus those in DKD patients in group A. It suggests that implementation of nursing intervention for DKD patients can effectively help patients improve and control the level of renal function, while ultrasound images based on intelligent algorithm can dynamically detect the changes in the level of renal function in patients, which has the value of clinical promotion.

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer.

The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was DSC (Dice similarity coefficient) = 0.9626, MPA (mean pixel accuracy) = 0.9676, and IOU (intersection over union) = 0.9155. When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, P > 0.05. After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics (P < 0.05). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.

Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.

A Hemolysis Image Detection Method Based on GAN-CNN-ELM.

Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolutional neural networks (CNNs) with extreme learning machine (ELM) is proposed. First, the image enhancement and data enhancement are performed on a sample set, and GAN is used to expand the sample data volume. Second, CNN is used to extract the feature vectors of the processed images and label eigenvectors with one-hot encoding. Third, the feature matrix is input to the map in the ELM network to minimize the error and obtain the optimal weight by training. Finally, the image to be detected is input to the trained model, and the image with the greatest probability is selected as the final category. Through model comparison experiments, the results show that the hemolysis image detection method based on the GAN-CNN-ELM model is better than GAN-CNN, GAN-ELM, GAN-ELM-L1, GAN-SVM, GAN-CNN-SVM, and CNN-ELM in accuracy and speed, and the accuracy rate is 98.91%.

The Effects of Rso2 and PI Monitoring Images on the Treatment of Premature Infants Based on Deep Learning.

In recent years, due to the combined effects of individual behavior, psychological factors, environmental exposure, medical conditions, biological factors, etc., the incidence of preterm birth has gradually increased, so the incidence of various complications of preterm infants has also become higher and higher. This article is aimed at studying the therapeutic effects of preterm infants and proposing the application of rSO2 and PI image monitoring based on deep learning to the treatment of preterm infants. This article introduces deep learning, blood perfusion index, preterm infants, and other related content in detail and conducts experiments on the treatment of rSO2 and PI monitoring images based on deep learning in preterm infants. The experimental results show that the rSO2 and PI monitoring images based on deep learning can provide great help for the treatment of preterm infants and greatly improve the treatment efficiency of preterm infants by at least 15%.

Measurement of Body Surface Area for Psoriasis Using U-net Models.

During the evaluation of body surface area (BSA), precise measurement of psoriasis is crucial for assessing disease severity and modulating treatment strategies. Physicians usually evaluate patients subjectively through direct visual evaluation. However, judgment based on the naked eye is not reliable. This study is aimed at evaluating the use of machine learning methods, specifically U-net models, and developing an artificial neural network prediction model for automated psoriasis lesion segmentation and BSA measurement. The segmentation of psoriasis lesions using deep learning is adopted to measure the BSA of psoriasis so that the severity can be evaluated automatically in patients. An automated psoriasis lesion segmentation method based on the U-net architecture was used with a focus on high-resolution images and estimation of the BSA. The proposed method trained the model with the same patch size of 512 × 512 and predicted testing images with different patch sizes. We collected 255 high-resolution psoriasis images representing large anatomical sites, such as the trunk and extremities. The average residual of the ground truth image and the predicted image was approximately 0.033. The interclass correlation coefficient between the U-net and dermatologist's segmentations measured in the ratio of affected psoriasis over the body area in the test dataset was 0.966 (95% CI: 0.981-0.937), indicating strong agreement. Herein, the proposed U-net model achieved dermatologist-level performance in estimating the involved BSA for psoriasis.

Research on Diagnosis Architecture of Cardiovascular Diseases Based on Multimedical Images.

OBJECTIVE: To study the effect of a multi-image source 3D modeling imaging examination system on the diagnosis of cardiovascular diseases in cardiac surgery. METHODS: The data of 680 confirmed patients and 1590 suspected patients in the cardiac surgery department of all hospitals of a large chain hospital management group were selected. All patients gave the examination results of multiple image sources and independent examination results of multiple image sources, respectively, their examination sensitivity, specificity, and reliability were compared, and the treatment efficiency and nursing satisfaction of the virtual reference group were deduced in MATLAB. Perform the bivariate t-test and comparative statistics in SPSS. RESULTS: The multi-image source 3D modeling examination system had higher examination sensitivity, specificity, and reliability and higher examination sensitivity in the early stage of the disease. It was deduced that the clinical efficiency and nursing satisfaction based on the examination results were significantly improved (t < 10.000, p < 0.01). CONCLUSION: The multi-image source 3D modeling imaging examination system is suitable for the diagnosis of cardiovascular diseases in cardiac surgery.

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury.

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft's residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians' artificial diagnoses. The results showed that accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians' artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians' artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than AUCs of doctors' diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with the statistically significant difference (P < 0.05). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively (P > 0.05). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians' artificial judgments and had great clinical value for the diagnoses and treatments of the disease.

Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images.

Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.

Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study.

Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the clinician, health system, and the patient in leishmaniasis endemic countries. In this study, image processing was initially done using 40 CL lesion color images that were captured using a mobile phone camera, to establish a technique to extract features from the image which could be related to the clinical status of the lesion. The identified techniques were further developed, and ten ulcer images were analyzed to detect the extent of inflammatory response and/or signs of healing using pattern recognition of inflammatory tissue captured in the image. The images were preprocessed at the outset, and the quality was improved using the CIE L∗a∗b color space technique. Furthermore, features were extracted using the principal component analysis and profiled using the signal spectrogram technique. This study has established an adaptive thresholding technique ranging between 35 and 200 to profile the skin lesion images using signal spectrogram plotted using Signal Analyzer in MATLAB. The outcome indicates its potential utility in visualizing and assessing inflammatory tissue response in a CL ulcer. This approach is expected to be developed further to a mHealth-based prediction algorithm to enable remote monitoring of treatment response of cutaneous leishmaniasis.

Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection.

Based on the ultrasonic imaging and endoscopic resection of the intelligent segmentation algorithm, this study is aimed at exploring whether nursing intervention can promote the good recovery of patients with colon polyps, hoping to find a new method for clinical treatment of the colon polyps. Patients with colon polyps were divided into an experimental group (fine nursing) and a control group (general nursing). The colonoscopy polyp ultrasound image was preprocessing to select the seed points and background points. The random walk decomposition algorithm was applied to calculate the probability of each marked point, and then, the marked image was outputted. The accuracy of the intelligent segmentation algorithm was 81%. The incidence of complications in the experimental group was 4.83%, which was lower than 16.66% in the control group, and the difference was statistically obvious (P < 0.05). Perioperative refined nursing intervention for colon polyp patients undergoing endoscopic electrosurgical resection can decrease postoperative adverse reactions; reduce postoperative mucosal perforation, blood in the stool, abdominal pain, and small bleeding; lower the incidence of postoperative complications; and allow patients to recover quickly, enhancing the life comfort of patient.

Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main symptoms of this condition. These symptoms frequently result in difficulty doing a variety of tasks. As a result, preliminary cataract detection and prevention may help to minimize the rate of blindness. This paper is aimed at classifying cataract disease using convolutional neural networks based on a publicly available image dataset. In this observation, four different convolutional neural network (CNN) meta-architectures, including InceptionV3, InceptionResnetV2, Xception, and DenseNet121, were applied by using the TensorFlow object detection framework. By using InceptionResnetV2, we were able to attain the avant-garde in cataract disease detection. This model predicted cataract disease with a training loss of 1.09%, a training accuracy of 99.54%, a validation loss of 6.22%, and a validation accuracy of 98.17% on the dataset. This model also has a sensitivity of 96.55% and a specificity of 100%. In addition, the model greatly minimizes training loss while boosting accuracy.

Clinical Research of Orthopedic Trauma Based on Computer 3D Image Analysis.

In order to improve the clinical research effect of orthopedic trauma, this paper applies computer 3D image analysis technology to the clinical research of orthopedic trauma and proposes the BOS technology based on FFT phase extraction. The background image in this technique is a "cosine blob" background image. Moreover, this technology uses the FFT phase extraction method to process this background image to extract the image point displacement. The BOS technology based on FFT phase extraction does not need to select a diagnostic window. Finally, this paper combines computer 3D image analysis technology to build an intelligent system. According to the experimental research results, the clinical analysis system of orthopedic trauma based on computer 3D image analysis proposed in this paper can play an important role in the clinical diagnosis and treatment of orthopedic trauma and improve the diagnosis and treatment effect of orthopedic trauma.

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In the preprocessing stage, the median filter has been used in order to remove salt-and-pepper noise because MRI images are normally affected by this type of noise, the grayscale images are also converted to RGB images in this stage. In the preprocessing stage, the histogram equalization has also been used to enhance the quality of each RGB channel. In the feature extraction stage, the three channels, namely, red, green, and blue, are extracted from the RGB images and statistical measures, namely, mean, variance, skewness, kurtosis, entropy, energy, contrast, homogeneity, and correlation, are calculated for each channel; hence, a total of 27 features, 9 for each channel, are extracted from an RGB image. After the feature extraction stage, different machine learning algorithms, such as artificial neural network, k-nearest neighbors' algorithm, decision tree, and Naïve Bayes classifiers, have been applied in the classification stage on the features extracted in the feature extraction stage. We recorded the results with all these algorithms and found that the decision tree results are better as compared to the other classification algorithms which are applied on these features. Hence, we have considered decision tree for further processing. We have also compared the results of the proposed method with some well-known algorithms in terms of simplicity and accuracy; it was noted that the proposed method outshines the existing methods.

Analysis on Characteristics of Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis of Cerebral Aneurysm.

This study was to explore the effect of a low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on magnetic resonance imaging (MRI) images of patients with cerebral aneurysm and to evaluate the practical value of the LRMD algorithm in the clinical diagnosis of cerebral aneurysm. In this study, the intracranial MRI data of 40 patients with cerebral aneurysm were selected to study the denoising effect of the low-rank matrix denoising algorithm based on the Gaussian mixture model on MRI images of cerebral aneurysm under the influence of Rice noise, to evaluate the PSNR value, SSIM value, and clarity of MRI images before and after denoising. The diagnostic accuracy of MRI images of cerebral aneurysms before and after denoising was compared. The results showed that after the low-rank matrix denoising algorithm based on the Gaussian mixture model, the PSNR, SSIM, and sharpness values of intracranial MRI images of 10 patients were significantly improved (P < 0.05), and the diagnostic accuracy of MRI images of cerebral aneurysm increased from 76.2 ± 5.6% to 93.1 ± 7.9%, which could diagnose cerebral aneurysm more accurately and quickly. In conclusion, the MRI images processed based on the low-rank matrix denoising algorithm under the Gaussian mixture model can effectively remove the interference of noise, improve the quality of MRI images, optimize the accuracy of MRI image diagnosis of patients with cerebral aneurysm, and shorten the average diagnosis time, which is worth promoting in the clinical diagnosis of patients with cerebral aneurysm.