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To evaluate the retrieval performance and compare the traditional CBIR algorithm against SE-CBIR, we used the retrieval precision for k retrieved images (P@k) or cut-off value metric:
Similarly, for multiclass retrieval, we defined the average precision for k retrieved images (AP@k):
with M being the total number of classes.
To evaluate the impact of this type of decision support, a reader study was designed within the University Hospital Zurich Dermatology Clinic.
JMIR Dermatol 2023;6:e42129
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The 9 superfeatures (dots, globules, lines, network structures, patterns, regression structures, shiny white structures, structureless areas, and vessels; shown in Multimedia Appendix 1, Table S1) were presented to participants during the tutorial on the Diagnos Us smartphone app, adapted from Marghoob and Braun [41].
To measure agreement across raters, we employed the Cohen κ [42], which has a value of 0 for completely random choices, increasing toward a maximum value of 1.0 with improved IRR.
JMIR Med Inform 2023;11:e38412
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