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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