Preprocessing combinations improve deep learning for colorectal cancer detection

Standardization scaling significantly improves deep learning performance for colorectal cancer detection compared to normalization. Larger input patch sizes enhance model accuracy by capturing more spatial context, while noise reduction unexpectedly degrades performance. Blood filtering is more effective than filtering reflected light. The study highlights the critical need for careful selection of preprocessing techniques to maximize deep learning model performance in medical imaging applications.