Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors

Abstract

The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial ( P < 0.05 ). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm ( P < 0.05 ). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area ( P < 0.05 ). FA of stage III+IV was smaller than those of stage I and II ( P < 0.05 ), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II ( P < 0.05 ). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.

MRI Brain Tumor Prediction using Azure Streamlit Framework and Analysis of CNN Activation Functions

“…The accuracy obtained with CNN SoftMax fully connected layer is 98.67%; with radial basis and decision tree was having accuracy of 97.34% and 94.24%, respectively (13) . Wang et al (14) proposed fuzzy C-Means algorithm with an ARM Linux embedded system for brain tumor prediction using MRI images; discovered that evaluation metrics for optimized fuzzy C-Means are better than the deterministic C-Means clustering algorithm and traditional fuzzy C-Means. An ultra-light deep learning architecture was proposed with a Grey Level Co-occurrence Matrix (GLCM).…”