Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense micro-block difference feature (DMDF) into a unified...

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Bibliographic Details
Published in:	Journal of medical systems Vol. 43; no. 6; pp. 152 - 9
Main Authors:	Deng, Wu, Shi, Qinke, Luo, Kai, Yang, Yi, Ning, Ning
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2019 Springer Nature B.V
Subjects:	Algorithms Artificial neural networks Brain Brain cancer Brain Neoplasms - pathology Brain tumors Cancer Critical components Deep Learning Distributed Analytics and Deep Learning in Health Care Feature maps Health Informatics Health Sciences Humans Image & Signal Processing Image processing Image Processing, Computer-Assisted - methods Image segmentation Machine learning Magnetic Resonance Imaging Medical imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Neuroimaging Rotation Statistics for Life Sciences Texture Tumors Rotation invariant Tumor segmentation Convolutional neural network Non-quantifiable local feature;dense micro-block difference
ISSN:	0148-5598, 1573-689X, 1573-689X
Online Access:	Get full text
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