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(searched for: Machine and Deep Learning Methods Enable the Accurate and Efficient Segmentation, Grading, Diagnosis and Prognosis of Brain Tumors)
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Mohsen Tabatabaei, Zeinab Khazaei, Kamran Tavakol, Andrea Tavakol
Journal of Radiology and Clinical Imaging, Volume 3, pp 58-63; doi:10.26502/jrci.2809027

Abstract:
Early diagnosis and management of brain tumors are crucial and save patient from adverse effects or even death. Computer science advances have been made such that artificial intelligence (AI) brings enhancement to the classification, segmentation, grading and staging of brain tumors while assisting in the prediction of the diagnosis, treatment, future care and the prognosis, based on the analysis of MRI data. This review is aimed at enhancing the technical awareness of radiology residents, practitioners and physicians in general medicine, neurology and neurosurgery. Also, researchers from computer science, neuroscience and other relevant areas will find this review very helpful. We searched for relevant articles in Google Scholar, Scopus, and Pubmed, using these keywords: artificial intelligence, machine learning, deep learning, brain tumors, magnetic resonance imaging, MRI, and radiological image analyses. Out of 138 articles, 17 were selected, which described various AI algorithms to analyze brain tumor MRI data. This review presents the significant findings and conclusions of the selected articles, and the role of machine and deep learning methods in the classification, segmentation, diagnosis and prognosis of brain tumors. These AI methods enable radiologists to perform accurate, rapid and efficient mapping, classification, segmentation, grading and staging of brain tumors while predicting the diagnosis, treatment, future care and the clinical prognosis of patients with brain tumors.
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