Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN)
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3877-3
Abstract: Computed tomography (CT) imaging is the preferred imaging modality for diagnosing lung-related complaints. Automatic lung segmentation is the most common prerequisite to develop a computerized diagnosis system for analyzing chest CT images. In this paper,… read more here.
Keywords: deep wide; segmentation; lung segmentation; automatic lung ... See more keywords
Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network
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DOI: 10.1016/j.measurement.2020.107619
Abstract: Abstract In order to denoise the raw signal and fuse multiple sources of information for the fault diagnosis of reciprocating compressor, this paper proposes a novel convolutional deep belief network-based method and employs a novel… read more here.
Keywords: fault; network; diagnosis; deep belief ... See more keywords
ECG classification using 1-D convolutional deep residual neural network
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DOI: 10.1371/journal.pone.0284791
Abstract: An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning… read more here.
Keywords: ecg classification; deep residual; classification; ecg ... See more keywords
Automatic text-independent speaker verification using convolutional deep belief network
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DOI: 10.18287/2412-6179-co-621
Abstract: This paper is devoted to the use of the convolutional deep belief network as a speech feature extractor for automatic text-independent speaker verification. The paper describes the scope and problems of automatic speaker verification systems.… read more here.
Keywords: speaker; convolutional deep; speaker verification; deep belief ... See more keywords