Title
Colorectal Polyp Image Detection And Classification Through Grayscale Images And Deep Learning
Abstract
Colonoscopy screening and colonoscopic polypectomy can decrease the incidence and mortality rate of colorectal cancer (CRC). The adenoma detection rate and accuracy of diagnosis of colorectal polyp which vary in different experienced endoscopists have impact on the colonoscopy protection effect of CRC. The work proposed a colorectal polyp image detection and classification system through grayscale images and deep learning. The system collected the data of CVC-Clinic and 1000 colorectal polyp images of Linkou Chang Gung Medical Hospital. The red-green-blue (RGB) images were transformed to 0 to 255 grayscale images. Polyp detection and classification were performed by convolutional neural network (CNN) model. Data for polyp detection was divided into five groups and tested by 5-fold validation. The accuracy of polyp detection was 95.1% for grayscale images which is higher than 94.1% for RGB and narrow-band images. The diagnostic accuracy, precision and recall rates were 82.8%, 82.5% and 95.2% for narrow-band images, respectively. The experimental results show that grayscale images achieve an equivalent or even higher accuracy of polyp detection than RGB images for lightweight computation. It is also found that the accuracy of polyp detection and classification is dramatically decrease when the size of polyp images small than 1600 pixels. It is recommended that clinicians could adjust the distance between the lens and polyps appropriately to enhance the system performance when conducting computer-assisted colorectal polyp analysis.
Year
DOI
Venue
2021
10.3390/s21185995
SENSORS
Keywords
DocType
Volume
colorectal polyp, grayscale image, colonoscopy, convolutional neural network, computer-assisted colorectal polyp analysis
Journal
21
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chen-Ming Hsu1775.77
Chien-Chang Hsu27611.68
Zhe-Ming Hsu300.34
Feng-Yu Shih400.34
Meng-Lin Chang500.34
Tsung-Hsing Chen600.34