Fig 1.
Study flowchart.
Fig 2.
Process for obtaining serial computed tomography (CT) images with red, green, blue (RGB) channel superposition.
The RGB superposition method enhances the differences in the connectivity and shape of the target in serial CT images. The connected region of a continuous slice is almost gray in color, whereas the unconnected region(s) show(s) different shapes (i.e., primary colors). A three-dimensional effect can be obtained from two-dimensional images based on the representation of the connectivity information pertaining to the organization that can be confirmed by the three-dimensional volume using the RGB color model.
Fig 3.
(a) EfficientNet-B0 architecture (b) Example of a prediction outcome label.
Table 1.
Characteristics of the datasets constructed in this study.
Table 2.
Train, test, and validation case and data.
Fig 4.
Area under the receiver operating characteristic curve for classifying acute appendicitis (blue), acute diverticulitis (orange), and normal appendix (green) using (a) the single image and (b) serial image red, green, blue (RGB) methods.
Table 3.
Diagnostic performance of CNN classification using single and RGB methods for acute appendicitis and acute diverticulitis.
Fig 5.
Examples of acute appendicitis cases correctly classified by the convoluted neural network by (CNN) using the (a) single image and (b) red, green, blue (RGB) methods, with and without class activation maps (CAMs).
Fig 6.
Examples of acute diverticulitis cases correctly classified by the convoluted neural network (CNN) using the (a) single image and (b) red, green, blue (RGB) methods, with and without class activation maps (CAMs).
Fig 7.
Examples of normal appendix cases correctly classified by the convoluted neural network (CNN) using the (a) single image and (b) red, green, blue (RGB) methods, with and without class activation maps (CAMs).
Table 4.
Differences between the single and RGB methods for the data analyses.