A Study on Preprocessing Techniques Used for Gastrointestinal Cancer Detection in Endoscopy Images

This study paper focuses on diagnosis of gastrointestinal (GI) cancer through the systematic preprocessing and analysis of endoscopic images. Usually the raw endoscopic data suffers from problems such as poor lighting condition, motion blur and artifacts like bubbles or reflections. These kinds of problems affect the clarity and quality of image. These limitations are overcome by applying preprocessing techniques to enhance diagnosis reliability by noise removal and quality enhancement. This outcome images are then evaluated through advanced computational methods. This study paper discusses various methods and preprocessing techniques to improve the quality of the image for further processing like feature extraction and segmentation. From this study median filtering provides strong results for noise reduction and improves quality of the image. This approach significantly strengthens diagnostic process and strengthens clinical decision making for gastrointestinal cancer detection.