Magnetic Resonance Imaging (MRI) is commonly used in the diagnosis of brain tumors. Traditionally, physicians are trained to use their eyes when interpreting the scans; as the old saying goes, "a radiologist looking for a ruler is a radiologist in trouble." The recent emergence of the field of radiomics – which quantifies tumor features in MRI scans to create mineable high-dimensional data – is changing the game and creating new clinical decision support tools for identifying cases with tumor similarities. This paper leverages deep learning to match MRI tumor images to scans with similar features with the goal of providing clinicians a tool to increase accuracy and speed to diagnosis. The methodology presented herein involves combining multiple artificial neural networks in order to identify tumor regions and then match the tumor in question to those with the highest degree of similarities.
"WWT Research reports provide in-depth analysis of the latest technology and industry trends, solution comparisons and expert guidance for maturing your organization's capabilities. By logging in or creating a free account you’ll gain access to other reports as well as labs, events and other valuable content."
Thanks for reading. Want to continue?
Log in or create a free account to continue viewing MRI Radiomics: Tumor Identification and Reverse Image Search Using Convolutional Neural Network and access other valuable content.