Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy

Abstract EN

Background and Objective.

Colorectal cancer (CRC) is a common gastrointestinal tumour with high morbidity and mortality.

Endoscopic examination is an effective method for early detection of digestive system tumours.

However, due to various reasons, missed diagnoses and misdiagnoses are common occurrences.

Our goal is to use deep learning methods to establish colorectal lesion detection, positioning, and classification models based on white light endoscopic images and to design a computer-aided diagnosis (CAD) system to help physicians reduce the rate of missed diagnosis and improve the accuracy of the detection rate.

Methods.

We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy.

The convolutional neural network model is used to detect whether the image contains lesions: CRC, colorectal adenoma (CRA), and colorectal polyps.

The accuracy, sensitivity, and specificity rates are used as indicators to evaluate the model.

Then, the instance segmentation model is used to locate and classify the lesions on the images containing lesions, and mAP (mean average precision), AP50, and AP75 are used to evaluate the performance of an instance segmentation model.

Results.

In the process of detecting whether the image contains lesions, we compared ResNet50 with the other four models, that is, AlexNet, VGG19, ResNet18, and GoogLeNet.

The result is that ResNet50 performs better than several other models.

It scored an accuracy of 93.0%, a sensitivity of 94.3%, and a specificity of 90.6%.

In the process of localization and classification of the lesion in images containing lesions by Mask R-CNN, its mAP, AP50, and AP75 were 0.676, 0.903, and 0.833, respectively.

Conclusion.

We developed and compared five models for the detection of lesions in white light endoscopic images.

ResNet50 showed the optimal performance, and Mask R-CNN model could be used to locate and classify lesions in images containing lesions.