Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Tang, Chia-Pei; Chen, Kaihong; Lin, Tu-Liang

doi:10.3390/s21165315

Cited by 16 publications

(6 citation statements)

References 39 publications

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“…Polyps can be found with this pre-processing as image resolution increases. Transfer learning is advised for high-resolution colonoscopy polyp identification by Tang et al [25]. The identification of the polyps was precise but not categorical.…”

Section: Related Workmentioning

confidence: 99%

GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification With Deep Learning Approach

Yasmin,

Hassan,

Hasan

et al. 2023

IEEE Access

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The early detection of digestive problems is essential for lowering the chance of acquiring any form of gastrointestinal cancer, including esophageal cancer. Endoscopy is the method that is used the majority of the time for the purpose of examining and taking photos of this sort of illness. The application of artificial intelligence is now proving to be very efficient in enhancing the identification of gastrointestinal polyps and other abnormal features located inside the gastrointestinal system. As a direct consequence of this development, the use of AI within this sector has seen substantial growth. In the framework of artificial intelligence, this research investigates how well various types of algorithms perform in terms of polyp and abnormal feature recognition accuracy, efficiency, and detection. And introduced a model in this work that is GastroNet. It is developed by doing hyperparameter fine-tuning on YOLOv5 in order to find specific polyps and abnormal characteristics, particularly esophagitis. In this method, a single neural network is used to do an analysis on the whole picture before it is disassembled into its component parts and the bounding boxes and probabilities for each one are calculated independently. The goal of the hyperparameter fine-tuning is to further enhance the overall optimization of the model. Two different methods of annotation were used on a collection of data that consisted of one thousand separate images that needed to be labelled. In addition to implementing the fine-tuned SSD model, this study used three distinct backbone networks: MobileNet v2, MobileNet v2 FPN Lite, and Resnet50 v1 FPN. Additionally, this study has used CSPdarknet53 to create the improved YOLOv4 model. The results of the studies demonstrate that the proposed model, GastroNet was effective in correctly recognizing polyps and aberrant characteristics, reaching a high mAP (mean Average Precision), F1 score, and precision with a value of 0.99 and recall with a value of 1.00. The findings of this research will be a great help to physicians in the proper identification and diagnosis of abnormal features and gastrointestinal polyps.

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Section: Related Workmentioning

confidence: 99%

GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification With Deep Learning Approach

Yasmin,

Hassan,

Hasan

et al. 2023

IEEE Access

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“…Tang et al proposed a polyp detection method based on transfer learning technology for high-resolution colonoscopy images. The experimental results show that the polyp detection model can have high accuracy for polyp detection, but it had no obvious effect on polyp type classification [ 36 ]. Shen et al proposed a transformer convolution network for end-to-end polyp detection.…”

Section: Related Workmentioning

confidence: 99%

Polyp Detection from Colorectum Images by Using Attentive YOLOv5

Wan

Chen

2021

Diagnostics

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Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.

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“…This strategy is based on real-time polyp classification, which is cost-effective and saves time for endoscopists. Methods have been developed to enhance the early detection and classification of colon polyps [8,9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

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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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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Cited by 16 publications

References 39 publications

GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification With Deep Learning Approach

GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification With Deep Learning Approach

Polyp Detection from Colorectum Images by Using Attentive YOLOv5

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

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