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Computerized Tomography Images Processing Using Artificial Intelligence Techniques

Abstract:

The study and analysis of images have been increased with time due to the extraction of different features. This research area is leadership by Image Processing because it contributes with interesting techniques and many advances. There are several applications in image processing, such as medicine, agriculture, sports, and object recognition. In medicine, images reveal their importance to improve the processes to treat patients. It also helps the doctors to make better diagnostic. Besides, the environment of medical images is vast and includes x-ray, magnetic resonance, and computerized tomography images. However, the usage of CT images needs much medical staff to detect the organ with disease or with any problem. Thus, it is necessary to apply segmentation methods to improve diagnostics or disease detection. Methods that involve image segmentation require an in-depth study of traditional and non-traditional methods. In that sense, Artificial Intelligence (AI) methods are crucial in the studies of CT image segmentation due to those methods automated all of the processes. The majority contributions of AI are the novel methods from its subfields of Deep Learning and Neural Networks. They facilitate the processes through the usage of algorithms with learning and pbkp_rediction. It is essential to mention that Image processing techniques need to work with AI techniques to solve segmentation problems and improve the precision of the results. Therefore this paper aims to present state-of-the-art image segmentation using Computed Tomography images.