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I have a question in regards to merging features extracted from CNN and handcrafted features. I have been reading this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002895/#B33-sensors-22-02467 I am not sure if I am missing something but it seems that features are extracted from a CNN (ResNet34 extracts 512 features) which are passed from a sigmoid and then merged with handcrafted features (11 features). Then the merged features are passed from a fc layer (1000) and then perform binary classification based on the output.

My question is, what is the 1000 in the fc layer represent because I would expected that we have 523 features passed in the fc layer and the output to be binary.

Thank you

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