Abstract
In this paper, we propose a deep learning-based method for brain tumor classification. It is composed of two parts. The first part is brain tumor segmentation on the multimodal magnetic resonance image (mMRI), and the second part performs tumor classification using tumor segmentation results. A 3D deep neural network is implemented to differentiate tumor from normal tissues, subsequentially, a second 3D deep neural network is developed for tumor classification. We evaluate the proposed method using pateint dataset from Computational Precision Medicine: Radiology-Pathology Challenge (CPM: Rad-Path) on Brain Tumor Classification 2019. The result offers 0.749 for dice score and 0.764 for F1 score for validation data, while 0.596 for dice score and of 0.603 for F1 score for testing data, respectively. Our team was ranked second in the CPM:Rad-Path challenge on Brain Tumor Classification 2019 based on overall testing performance.
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This work was partially funded through NIH/NIBIB grant under award number R01EB020683.
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Pei, L., Vidyaratne, L., Hsu, WW., Rahman, M.M., Iftekharuddin, K.M. (2020). Brain Tumor Classification Using 3D Convolutional Neural Network. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_33
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