Classification of noiseless corneal image using capsule networks
| Autor: | H. James Deva Koresh, Shanty Chacko |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Computational intelligence Pattern recognition 02 engineering and technology Image enhancement Theoretical Computer Science Image (mathematics) Data set 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Geometry and Topology Sensitivity (control systems) Artificial intelligence business Software |
| Zdroj: | Soft Computing. 24:16201-16211 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-020-04933-5 |
| Popis: | Classifying a particular image from a data set is a complex work for any image analyst. Generally, the output of medical image scan gives numerous images for analysis. In that, the image analyst has to manually predict a better noiseless image for computer-assisted image process program. Manual verification of all the output images from the scan device consumes a lot of time in predicting the abnormality of a patient. The proposed capsule network for noiseless image algorithm assists the image analyst by classifying the noiseless image from the data set for further computer-assisted image enhancement or segmentation program. The proposed algorithm performance is evaluated and compared with the existing algorithms in terms of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. |
| Databáze: | OpenAIRE |
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