A computer-implemented determination method for determining the structure of a convolutional neural network, comprising: the acquisition step of acquiring N filters serving as the initial values, the N filters having the weights trained using a training image group, where N is a natural number greater than or equal to 1; the splitting step of increasing the number of the filters from N to M, where M is a natural number greater than or equal to 2 and is greater than N, by adding a filter obtained by performing a transformation used in image processing on at least one of the N filters, wherein the splitting step includes a step of causing the M filters to learn the weights using the training image group, and, thereafter, a split evaluation step of evaluating the identification performance of the M filters, which learned the weights by using the training image group, wherein, if the identification performance evaluated in the split evaluation step is less than or equal to the identification performance of the N filters, the splitting step is performed again; and the merge step of merging the M filters into L filters, where L is a natural number greater than or equal to 1 and is less than M, by clustering the M filters and selecting a filter located at the center of each of the clusters; a step of causing the L filters after being merged to learn the weights using the training image group; and a merge evaluation step of evaluating the identification performance of the L filters, which learned the weights by using the training image group, wherein, if the identification performance evaluated in the merge evaluation step is less than or equal to the identification performance of the M filters, the merging step is performed again. the acquisition step of acquiring N filters serving as the initial values, the N filters having the weights trained using a training image group, where N is a natural number greater than or equal to 1; the splitting step of increasing the number of the filters from N to M, where M is a natural number greater than or equal to 2 and is greater than N, by adding a filter obtained by performing a transformation used in image processing on at least one of the N filters, wherein the splitting step includes a step of causing the M filters to learn the weights using the training image group, and, thereafter, a split evaluation step of evaluating the identification performance of the M filters, which learned the weights by using the training image group, wherein, a step of causing the M filters to learn the weights using the training image group, and, thereafter, a split evaluation step of evaluating the identification performance of the M filters, which learned the weights by using the training image group, wherein, if the identification performance evaluated in the split evaluation step is less than or equal to the identification performance of the N filters, the splitting step is performed again; and the merge step of merging the M filters into L filters, where L is a natural number greater than or equal to 1 and is less than M, by clustering the M filters and selecting a filter located at the center of each of the clusters; a step of causing the L filters after being merged to learn the weights using the training image group; and a merge evaluation step of evaluating the identification performance of the L filters, which learned the weights by using the training image group, wherein, if the identification performance evaluated in the merge evaluation step is less than or equal to the identification performance of the M filters, the merging step is performed again.