Artificial Neural Networks (ANN) have been applied to solve a large number of real-world problems, considerable complexity. Solving problems that are too complex for conventional technologies is the main advantage of ANN. In general, these problems include pattern recognition and forecasting. ANN have been used in the medical imaging, in computer aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration. In this paper we use ANN as a prediction method in medical images to complete the missing data in MRI and CT images. By using these methods, we can eliminate artifacts of image and visualize the new image which is much closer to the desired one. This image can be used for diagnostic purposes or radiotherapy.Artificial Neural Networks (ANN) have been applied to solve a large number of real-world problems, considerable complexity. Solving problems that are too complex for conventional technologies is the main advantage of ANN. In general, these problems include pattern recognition and forecasting. ANN have been used in the medical imaging, in computer aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration. In this paper we use ANN as a prediction method in medical images to complete the missing data in MRI and CT images. By using these methods, we can eliminate artifacts of image and visualize the new image which is much closer to the desired one. This image can be used for diagnostic purposes or radiotherapy.
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Medical image prediction using artificial neural networks
Semantic Scholar · Medicine · 2019
Abstract
Artificial Neural Networks (ANN) have been applied to solve a large number of real-world problems, considerable complexity. Solving problems that are too complex for conventional technologies is the main advantage of ANN. In general, these problems include pattern recognition and forecasting. ANN have been used in the medical imaging, in computer aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration. In this paper we use ANN as a prediction method in medical images to complete the missing data in MRI and CT images. By using these methods, we can eliminate artifacts of image and visualize the new image which is much closer to the desired one. This image can be used for diagnostic purposes or radiotherapy.Artificial Neural Networks (ANN) have been applied to solve a large number of real-world problems, considerable complexity. Solving problems that are too complex for conventional technologies is the main advantage of ANN. In general, these problems include pattern recognition and forecasting. ANN have been used in the medical imaging, in computer aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration. In this paper we use ANN as a prediction method in medical images to complete the missing data in MRI and CT images. By using these methods, we can eliminate artifacts of image and visualize the new image which is much closer to the desired one. This image can be used for diagnostic purposes or radiotherapy.