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

Chen-Ming Hsu¹, Chien-Chang Hsu^2,3, Zhe-Ming Hsu²

¹Department of Gastroenterology and Hepatology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No. 5, Fuxing St., Guishan Dist., Taoyuan City 333, Taiwan.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This study developed a deep learning system for colorectal polyp detection using grayscale images, achieving 95.1% accuracy. This method is effective for computer-assisted analysis, improving colorectal cancer screening.

Keywords:

colonoscopy colorectal polyp computer-assisted colorectal polyp analysis convolutional neural network grayscale image

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Area of Science:

Medical imaging
Artificial intelligence in oncology
Gastroenterology

Background:

Colorectal cancer (CRC) incidence and mortality can be reduced by colonoscopy screening and polypectomy.
The effectiveness of colonoscopy in preventing CRC is influenced by adenoma detection rates and polyp diagnostic accuracy, which vary among endoscopists.
Accurate and efficient polyp detection is crucial for improving colonoscopy's protective effect against CRC.

Purpose of the Study:

To propose a deep learning-based system for detecting and classifying colorectal polyps using grayscale images.
To evaluate the performance of this system compared to traditional RGB and narrow-band imaging.
To investigate the impact of image size on polyp detection and classification accuracy.

Main Methods:

A convolutional neural network (CNN) model was employed for polyp detection and classification.
Colorectal polyp images from CVC-Clinic and Linkou Chang Gung Medical Hospital were collected and converted to grayscale.
The system was trained and tested using 5-fold cross-validation on a dataset divided into five groups.

Main Results:

The system achieved a 95.1% accuracy for polyp detection using grayscale images, outperforming RGB (94.1%) and narrow-band images.
Diagnostic accuracy, precision, and recall for narrow-band images were 82.8%, 82.5%, and 95.2%, respectively.
Polyp detection and classification accuracy significantly decreased for images smaller than 1600 pixels.

Conclusions:

Grayscale images offer an equivalent or superior accuracy for polyp detection compared to RGB images, with the advantage of lightweight computation.
Image resolution is a critical factor; maintaining image size above 1600 pixels is recommended for optimal system performance.
Clinicians should optimize the lens-to-polyp distance during colonoscopy to enhance the performance of computer-assisted polyp analysis systems.