LAUSR

Colon cancer is the third most common cancer in the world, and it is increasingly threatening people’s health. Early diagnosis is crucial to reducing the threat; however, the chance of missed polyps in today’s colonoscopy examination is still high (about 10%) due to limitations in diagnosis techniques and data analysis methods. The colonoscope is a kind of robot and on its tip there is a camera to acquire images. This paper presents a study aimed to improve the rate of successful diagnosis with a new image data analysis approach based on the faster regional convolutional neural network (faster R-CNN). This new approach has two steps for data analysis: (i) pre-processing of images to characterize polyps, and (ii) incorporating of the result of the pre-processing into the faster R-CNN. Specifically, the pre-processing of colonoscopy was expected to reduce the influence of specular reflections, resulting in an improved image, upon which the faster R-CNN algorithm was aplied. There are several improvements of the faster r-CNN tailoring to the task of colon polyps detection. To confirm the superiority of this new approach, the mean average precision (mAP) was used to compare the results obtained with the new approach and the faster R-CNN algorithm. The experimental result shows that the mAP of the new approach is 91.43%, as opposed to 90.57% with the faster R-CNN, which shows a significant improvement.