Artificial Intelligence in Medical Image Processing - Xhako, Dafina; Hoxhaj, Suela; Hyka, Niko; Spahiu, Elda; Malkaj, Partizan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Neural networks have been used to solve an increasing number of very complex real-world problems. Their biggest advantage is by far their capacity to resolve problems that are too complex for conventional technologies. These problems are usually related to pattern recognition and forecasting. Artificial neural networks (ANNs) are being used in a variety of fields, including medical imaging, computer-aided diagnosis, medical image registration, segmentation, and edge detection for visual content analysis. In this work, we fill in the blanks in CT and MRI scan images by interpolating medical images using artificial neural networks (ANNs). In this way, we may remove artifacts from the image and see a new image that is much more similar to the original. It is feasible to use these processed images for diagnostic purposes or for radiation therapy. The exact implementation details could change depending on the interpolation task and the type of medical images. This technique explains how to use Matlab's Neural Network Toolbox, which makes it easier to create, train, and test neural networks for interpolation tasks, to improve the quality of images.