Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network
We present a holistic brain tumor screening and classification method for detecting and distinguishing multiple types of brain tumors on MR images. The challenges arise from the significant variations of location, shape, size, and contrast of these tumors. The proposed algorithms start with feature extraction from axial slices using dense convolutional neural networks; the obtained sequential features of multiple frames are then fed into a recurrent neural network for classification. Different from most other brain tumor classification algorithms, our framework is free from manual or automatic region of interests segmentation. The results reported on a public dataset and a population of 422 proprietary MRI scans diagnosed as normal, gliomas, meningiomas and metastatic brain tumors demonstrate the effectiveness and efficiency of our method.
This work was supported in part by NSF through grants IIS-1422591, CCF-1422324, and CCF-1716400 and by NSFC through grants 81771904 and 61828205. It was also supported in part by start-up funds (for Drs. Mingchen Gao and Changyou Chen) from the Department of Computer Science and Engineering, University at Buffalo, the State University of New York.
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