Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"2D CNN"”.
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Learning from videos with deep convolutional LSTM networks
… video sequences involve temporally processing 2D CNN features from the individual frames or directly utilizing 3D convolutions within high-performing 2D CNN architectures. The focus typically remains on how to incorporate the temporal processing within an already stable spatial architecture. …
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Hypoxic-ischemic encephalopathy grading using novel EEG signal processing and machine learning techniques
… for visual inspection. The two dimensional (2D) CNN is designed as a regressor to map the input image to a value on the HIE grading scale to enhance the model's ability to leverage the monotonic relationship between the grades. An optimised rounding function is implemented to define the final …
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Volumetric Medical Classification using Deep Learning: A comparative study on classifying Alzheimer's disease using Convolutional Neural Networks
… usage of 3D model architecture over traditional 2D architecture. In doing so, however, it is revealed that the 2D models do ultimately perform only slightly below the 3D model. Thus, the 2D approaches hold merit for potential usage, should a 2D planar approach be desired. The paper presents a …
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The Influence of AR Head-Mounted Displays on Spatial Perception and Worker Response in Construction Training
… environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, …
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Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques
… medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel …