Using categorical_crossentropy for binary classification
Binary cross-entropy is a special case of categorical cross-entropy with just 2 classes. So theoretically it does not make a difference.

If $y_k$ is the true label and $\hat{y}_k$ is the predicted label of class $k$ (both one-hot encoded, i.e. $\sum_k y_k =1 \land \sum_k \hat{y_k} =1 \land y_k,\hat{y_k} \in \{0,1\}$) the multicategorical cross-entropy for $K$ categories is
$$-\sum_{k=1}^{K}y_k \log(\hat{y}_k)$$

which is equal to the binary cross-entropy for $K=2$ :
$$-\sum_{k=1}^{K}y_k \log(\hat{y}_k) = -y \log(\hat{y})- (1-y) \log(1-\hat{y})$$

Just in case there are any implementation differences, e.g. speed-wise, I would still just use binary cross-entropy for a binary classification problem instead of multicategory with $K=2$.