It makes no sense. Convolutions keep spatial structure. Flattening everything would lead to a worse solution as you should fine a kernel such that applied to a tensor and flattened provides good features. That’s harder than applying fully connected layers.
Sometimes ppl pass max pooling features or average pooling features as classification scores.