Use of sigmoid function allows the network to introduce non-linear relationship into the model, which allows the neural network to learn more complex decision boundaries.

Like in image classification tasks, the sigmoid can be used to convert the output of the linear model into a probability. creating an output = 1, will create a bias as we are looking for probability.

take a look at this question, although it has low threshold of 0.2 we look for tumor suspicion based on the lower range as one does not want to miss out a tiny pathology. So when put an output = 1, we tend to create bias in such algorithm.

where as see the below image here the sigmoid function turns a regression line into decision boundary for binary classification. where in if x1 + x2 is less than 3 (for this image only as decision boundary can vary depending on the criteria of algorithm) y=0 and if x1 and x2 is greater than 3 y =1

Regards

DP