This study focused on the application of deep learning algorithms in the segmentation of CT images, so as to diagnose chronic kidney diseases accurately and quantitatively. First, the residual dual-attention module (RDA module) was used for automatic segmentation of renal cysts in CT images. 79 patients with renal cysts were selected as research subjects, of whom 27 cases were defined as the test group and 52 cases were defined as the training group. The segmentation results of the test group were evaluated factoring into the Dice similarity coefficient (DSC), precision, and recall. The experimental results showed that the loss function value of the RDA-UNET model rapidly decayed and converged, and the segmentation results of the model in the study were roughly the same as those of manual labeling, indicating that the model had high accuracy in image segmentation, and the contour of the kidney can be segmented accurately. Next, the RDA-UNET model achieved 96.25% DSC, 96.34% precision, and 96.88% recall for the left kidney and 94.22% DSC, 95.34% precision, and 94.61% recall for the right kidney, which were better than other algorithms. The results showed that the algorithm model in this study was superior to other algorithms in each evaluation index. It explained the advantages of this model compared with other algorithm models. In conclusion, the RDA-UNET model can effectively improve the accuracy of CT image segmentation, and it is worth of promotion in the quantitative assessment of chronic kidney diseases through CT imaging.
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.
This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 ( P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas ( P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.
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