A Framework for Brain Tumor Detection, Classification and Segmentation using Deep Learning
Rafia Ahsan1, Iram Shahzadi2,3, Ibtisam Aslam1,4, and Hammad Omer1
1Medical Image Processing Research Group (MIPRG), Dept. of Elect. & Comp. Engineering, COMSATS University Islamabad, Islamabad, Pakistan, 2OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Germany, 3German Cancer Research Center (DKFZ), Heidelberg, Germany, 4Service of Radiology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
Detection, classification and segmentation of brain tumor simultaneously is challenging due to the heterogeneous nature of the tumor. Limited work has been done in literature in this regard. The present study, therefore, aims to identify an object detection network that would be able to solve multi-class brain tumor classification and detection problem with high accuracy. Furthermore, the best performing detection network has been cascaded with 2D U-Net for pixel level segmentation. The proposed method not only classifies the tumor with high accuracy but also provides improved segmentation results compared to the standard U-Net.
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