AI Article Synopsis
- Automatic detection of carcinoma from hyperspectral images is crucial because these images can't be directly analyzed by clinicians.
- Traditional methods use many features, which can overload RAM and hinder performance on standard computers.
- The proposed spectral-spatial-variation (SSV) feature improves classification results for stomach carcinoma detection, achieving accuracy similar to using 72 spatial features.
Article Abstract
Automatic carcinoma detection from hyper/multi spectral images is of essential importance due to the fact that these images cannot be presented directly to the clinician. However, standard approaches for carcinoma detection use hundreds or even thousands of features. This would cost a high amount of RAM (random access memory) for a pixel wise analysis and would slow down the classification or make it even impossible on standard PCs. To overcome this, strong features are required. We propose that the spectral-spatial-variation (SSV) is one of these strong features. SSV is the residuum of the three dimensional hyper spectral data cube minus its approximation with a fitting in a small volume of the 3D image. By using it, the classification results of carcinoma detection in the stomach with multi spectral imaging will be increase significantly compared to not using the SSV. In some cases, the AUC can be even as high as by the usage of 72 spatial features.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525256 | PMC |
| http://dx.doi.org/10.1038/s41598-019-43971-4 | DOI Listing |
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