Author/Authors :
Gao, Junbo Shanghai Maritime University - Shanghai, China , Guo, Yuanhao Shanghai Maritime University - Shanghai, China , Sun, Yingxue Shanghai Maritime University - Shanghai, China , Qu, Guoqiang Department of Gastroenterology - Eastern Hospital - Shanghai Sixth People Hospital - Shanghai, China
Abstract :
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.
Keywords :
Early , Deep , Endoscopy , R-CNN
