Neural Network Algorithm MRI Images for Analysis of Influencing Factors for Patellar Dislocation in Exercise

Abstract

The study focused on segmentation effects of the improved algorithm of traditional neural network algorithm, small kernels two-path convolutional neural network (SK-TPCNN) combined with random forest (RF) algorithm on MRI images for patella, and the influencing factors of patellar dislocation during exercise. In this article, the MRI images for patellar dislocation patients were detected by virtue of the neural network algorithm, to establish the patella-related MRI image segmentation algorithm. In terms of dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity, the detection accuracy of MRI images for patella was evaluated, and the segmentation effect of MRI images for patella was assessed. 30 patients, who were diagnosed as patellar dislocation patients in hospital, were chosen as the research subjects. No matter whether the MRI images of the patients went through the processing of the neural network algorithm or not, all of them were analyzed. The results showed that, among the traditional neural network algorithm, SK-TPCNN algorithm, and SK-TPCNN + RF algorithm, the DSC values were 0.82, 0.71, and 0.79, respectively; the PPV values were 0.77, 0.59, and 0.85, respectively; and the sensitivity values were 0.79, 0.62, and 0.89, respectively. Obviously, the various parameters of the SK-TPCNN + RF algorithm were significantly greater than those of the SK-TPCNN algorithm, and the difference was statistically significant ( P < 0.05 ). It indicated that the segmentation ability of MRI images for patella of the NN algorithm was clearly improved, and the MRI image resolution was dramatically raised, which provided a referable basis for the MRI diagnosis of patients with patellar dislocation during exercise.

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

“…The MRI images are being segmented using fully automated mechanisms such as thresholding [ 19 ], region growing [ 20 ], and edge-based segmentation [ 21 – 23 ]. The threshold-based segmentation mechanism is suitable for MRI images, and the studies have proven that experimenting with diffusion-weighted MRI images has obtained 86% accuracy [ 24 ]. However, choosing the optimal threshold is challenging, and an inappropriate approximation may lead to poor segmentation.…”