Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network.
Sultan ImangaliyevMonique H. van der VeenCatherine M. C. VolgenantBruno G. LoosBart J. F. KeijserWim CrielaardEvgeni LevinPublished in: CoRR (2017)
Keyphrases
- convolutional neural network
- image classification
- image analysis
- image database
- image data
- three dimensional
- multiple images
- test images
- input image
- image collections
- image registration
- ground truth
- image retrieval
- pattern recognition
- segmentation method
- edge detection
- text classification
- face detection
- classification accuracy
- object recognition
- microscope images
- confocal microscopy
- pigmented skin lesions
- illumination conditions
- ccd camera
- preprocessing stage
- machine learning
- multi class
- d objects
- image features
- image regions
- similarity measure
- color images
- confocal laser scanning microscopy
- neural network
- computer vision
- feature selection
- fluorescence microscopy
- video sequences
- microscopy images
- extracted features
- visual features
- image matching
- remote sensing