| Title |
Automated Polyp Detection System in Colonoscopy using Object Detection Algorithm based on Deep Learning |
| Authors |
이정남(Jeong-nam Lee) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.152 |
| Keywords |
Colonoscopy; AutoAugment; CADｘ; Polyp Detection; YOLO |
| Abstract |
In Korea, colon cancer is increasing due to westernized eating habits. Colonoscopy is being used to reduce deaths from colon cancer
and studies of CADx(Computer-aided Diagnosis) are being developed to improve accuracy. Due to the nature of medical data, it was
difficult to collect a lot of data, so data was increased 25 times using AutoAugment’s CIFAR-10 policy, and YOLOv4(You Only
Look Once), a real-time object detection algorithm, was used to detect lesions. A new object detection algorithm, YOLOv4, use new
eight features such as Weighted-Residual-Connections, Cross-Stage-Partial-connections, Cross mini-Batch Normalization and SelfAdversarial-Training. The performance of augmented data had a maximum mAP of 27.44 higher than the original data. The average
IoU(Intersection over Union) was 11.44 higher than the original data. When the IoU value is 0.5, the F1-scores of the original data
and the augmented data are 0.9 and 0.97 respectively. |