Benchmarking human epithelial type 2 interphase cellsclassification methods on a very large dataset

Conclusions. We found that highest performance is obtained when using a strong classifier (typically a kernelised support vector machine) in conjunction with features extracted from local statistics. Furthermore, the misclassification profiles of the different methods highlight that some staining patterns are intrinsically more difficult to recognize. We also noted that performance is strongly affected by the fluorescence intensity level. Thus, low accuracy is to be expected when analyzing low contrasted images. Graphical abstract Highlights
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research