Detection of Polyps in Colonoscopic Videos Using Saliency Map-Based Modified Particle Filter

Sasmal, Pradipta; Bhuyan, M. K.; Gupta, S.; Iwahori, Yuji

doi:10.1109/tim.2021.3082315

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…However, contemporary methods increasingly make use of CNN and pre-trained networks. The Kvasir-SEG dataset [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], the CVC-ClinicDB dataset [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], and the TTA [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ] are frequently used datasets in analyses of polyp detection and segmentation networks.…”

Section: Related Workmentioning

confidence: 99%

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IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

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Section: Related Workmentioning

confidence: 99%

“…Sasmal et al [ 24 ] described a real-time monitoring method for segmenting polyp regions in colonoscopy video frames. The system used a modified tracking technique and a saliency map to locate the polyp region.…”

Section: Related Workmentioning

confidence: 99%

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Ahamed,

Syfullah,

Sarkar

et al. 2023

Sensors

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show abstract

“…The approximation-based PF method is particularly suitable to cope with nonlinear/uncertain/non-Gaussian systems for which the PDFs of the system states are difficult (or even impossible) to be accurately calculated. In practical systems, PFs have found many successful applications such as simultaneous localization and mapping [38], price prediction [29], corrosion quantification [1], polyp detection [33], and so on.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Particle Filtering Over Sensor Networks Under Protocols of Amplify-and-Forward and Decode-and-Forward Relays

Liu

Wang

Liu

et al. 2022

IEEE Trans. on Signal and Inf. Process. over Networks

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A saliency map-guided shape compactness for segmentation of polyps in colonoscopy images

Sasmal

Bhuyan

Iwahori

2022

SIViP

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Detection of Polyps in Colonoscopic Videos Using Saliency Map-Based Modified Particle Filter

Cited by 18 publications

References 30 publications

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Auxiliary Particle Filtering Over Sensor Networks Under Protocols of Amplify-and-Forward and Decode-and-Forward Relays

A saliency map-guided shape compactness for segmentation of polyps in colonoscopy images

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