Architecture for accurate polyp segmentation in motion-blurred colonoscopy images

Takamatsu, Makoto; Hasegawa, Makoto

doi:10.1109/icmla51294.2020.00175

Cited by 1 publication

(1 citation statement)

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“…Constant searches for polyps and movement of the colonoscope during the procedure cause polyp blurriness, which reduces the model's polyp detection ability. Studies have focused on endoscopic motion blurriness restoration [32][33][34][35]. The model is typically trained using high-definition images, and the detection rate decreases for blurred polyps.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computer-Aided Detection/Diagnosis System for Detection and Classification of Polyps in Colonoscopy

et al. 2023

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Using a deep learning algorithm in the development of a computer-aided system for colon polyp detection is effective in reducing the miss rate. This study aimed to develop a system for colon polyp detection and classification. We used a data augmentation technique and conditional GAN to generate polyp images for YOLO training to improve the polyp detection ability. After testing the model five times, a model with 300 GANs (GAN 300) achieved the highest average precision (AP) of 54.60% for SSA and 75.41% for TA. These results were better than those of the data augmentation method, which showed AP of 53.56% for SSA and 72.55% for TA. The AP, mAP, and IoU for the 300 GAN model for the HP were 80.97%, 70.07%, and 57.24%, and the data increased in comparison with the data augmentation technique by 76.98%, 67.70%, and 55.26%, respectively. We also used Gaussian blurring to simulate the blurred images during colonoscopy and then applied DeblurGAN-v2 to deblur the images. Further, we trained the dataset using YOLO to classify polyps. After using DeblurGAN-v2, the mAP increased from 25.64% to 30.74%. This method effectively improved the accuracy of polyp detection and classification.

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Section: Introductionmentioning

confidence: 99%