An improved deep learning approach and its applications on colonic polyp images detection.

BMC Medical Imaging
Wei WangJi Li

Abstract

Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator's experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children's hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children's hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. The accuracies of all models in datasets exceed 98%. The T...Continue Reading

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Citations

Jul 10, 2021·Computational Intelligence and Neuroscience·Wei WangXin Wang

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Methods Mentioned

BETA
feature extraction

Software Mentioned

VGGNets
VGGNet
CUDA10
Windows
ResNets
CuDNN
GAP
PyTorch

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