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Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy
- Source :
- Computational and Mathematical Methods in Medicine, Computational and Mathematical Methods in Medicine, Vol 2020 (2020)
- Publication Year :
- 2020
- Publisher :
- Hindawi Limited, 2020.
-
Abstract
- 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.
- Subjects :
- Adenoma
medicine.medical_specialty
Article Subject
Light
Colorectal cancer
Computer applications to medicine. Medical informatics
R858-859.7
Colonic Polyps
Colonoscopy
02 engineering and technology
Colorectal adenoma
Convolutional neural network
General Biochemistry, Genetics and Molecular Biology
Lesion
03 medical and health sciences
Deep Learning
0302 clinical medicine
Text mining
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Mass Screening
Segmentation
Diagnostic Errors
Medical diagnosis
General Immunology and Microbiology
medicine.diagnostic_test
business.industry
Applied Mathematics
Computational Biology
Intestinal Polyps
General Medicine
medicine.disease
Modeling and Simulation
020201 artificial intelligence & image processing
030211 gastroenterology & hepatology
Neural Networks, Computer
Radiology
medicine.symptom
Colorectal Neoplasms
business
Precancerous Conditions
Research Article
Subjects
Details
- ISSN :
- 17486718 and 1748670X
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Computational and Mathematical Methods in Medicine
- Accession number :
- edsair.doi.dedup.....f1e7c13d4166cd7edb55b0796d6309e3