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A Novel Computer-Aided Detection/Diagnosis System for Detection and Classification of Polyps in Colonoscopy.
- Source :
- Diagnostics (2075-4418); Jan2023, Vol. 13 Issue 2, p170, 20p
- Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 13
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 161434477
- Full Text :
- https://doi.org/10.3390/diagnostics13020170