MR Weighted Image Discrimination by Artificial Intelligence

Abstract

In this study, we proposed a method of learning neural networks by optimizing neural network input parameters to discern MRI-weighted images. To this end, we segmented the weighting domain of MRI. In feature extraction, the original image and segmented image were extracted by DWT, respectively. A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological technologist work together to provide a solution that is integrated with the radiologist's workflow.