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. 2021 Sep 7;21(18):5995.
doi: 10.3390/s21185995.

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning

Chen-Ming Hsu  1 , Chien-Chang Hsu  2   3 , Zhe-Ming Hsu  2 , Feng-Yu Shih  2 , Meng-Lin Chang  3 , Tsung-Hsing Chen  1
Affiliations

Colorectal Polyp Image Detection and Classification through Grayscale Images and Deep Learning

Chen-Ming Hsu et al. Sensors (Basel). .

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.

Keywords: colonoscopy; colorectal polyp; computer-assisted colorectal polyp analysis; convolutional neural network; grayscale image.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example image of NICE classification.
Figure 2
Figure 2
Excessive intestinal wall textures with small polyps.
Figure 3
Figure 3
Small texture of polyp and intestinal wall.
Figure 4
Figure 4
Colonoscopy image.
Figure 5
Figure 5
Segmented polyp image.
Figure 6
Figure 6
System architecture.
Figure 7
Figure 7
Polyp image before gray scaling.
Figure 8
Figure 8
Polyp image after gray scaling.
Figure 9
Figure 9
Colorectal polyp detection system architecture.
Figure 10
Figure 10
WL polyp detection.
Figure 11
Figure 11
NBI polyp detection.
Figure 12
Figure 12
ROC curve for classification.
Figure 13
Figure 13
Nonobvious polyp protrusions or similar to intestinal wall.
Figure 14
Figure 14
Excessive noise images.
Figure 15
Figure 15
Small pixel size polyp images.
Figure 16
Figure 16
Unobvious polyp textures.
Figure 17
Figure 17
Polyp image size below 1800 pixels.
See this image and copyright information in PMC

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