Deep Learning-Based Medical Image Preprocessing and Segmentation for Early Detection of Pancreatic Cancer.

Pancreatic cancer is one of the most serious and life-threatening diseases, mainly because it is often
detected at a late stage. Medical imaging techniques like CT scans help doctors identify tumors, but
accurately separating (segmenting) the pancreas and cancerous regions is still very challenging due to
low contrast, noise, and the complex structure of the organ. In this study, an automated approach is
proposed to improve the segmentation of pancreatic cancer using enhanced image preprocessing
techniques along with the U-Net deep learning model. The preprocessing step focuses on improving
image quality by reducing noise, adjusting intensity levels, and enhancing contrast, making the
important features more visible and easier to analyze. After preprocessing, the images are processed
using the U-Net model, which is well-known for its effectiveness in medical image segmentation. The
model helps in accurately identifying and outlining the tumor regions from the surrounding tissues. The
results show that this combined approach improves segmentation performance and provides more
reliable outputs.This work aims to support doctors by providing a more accurate and efficient tool for
detecting and analyzing pancreatic cancer, which can ultimately help in better diagnosis and treatment
planning.