@inproceedings {39584,
	title = {Automated Polyp Segmentation in Colonoscopy using MSRFNet},
	journal = {MediaEval medico },
	year = {2021},
	publisher = {CEUR Workshop Proceedings},
	abstract = {Colorectal cancer is one of the major cause of cancer-related death around the world. High-quality colonoscopy is considered mandatory for resecting and preventing colorectal cancers. In the recent past, various technological advances have been made towards improving the quality of colonoscopy. Despite the technical advancement, some polyps are frequently missed during colonoscopy examinations. Polyp detection ( for example, adenomas) rates are largely influenced by inter-endoscopist variability. Therefore, it is very challenging to standardize a high-quality colonoscopy. A computer-aided detection system could solve the problem with miss-detection. The {\textquoteleft}{\textquoteleft}MediaEval 2021{\textquoteright}{\textquoteright} challenge entails the chance to study and develop accurate automated polyp segmentation algorithms \cite{Hicks2021Medico}. In this paper, we propose our approach based on MSRFNet. Our experimental findings show that the model trained on the Kvasir-SEG dataset and evaluated on a competition test dataset obtains a dice coefficient of\&nbsp; 0.7055, Jaccard of 0.6176, a recall of 0.7293, and a precision of 0.7769. In addition to the MediaEval 2021 challenge, we evaluated our approach on the Endotect Challenge Dataset and {\textquoteleft}{\textquoteleft}2020 Medico Automatic Polyp Segmentation Challenge Dataset". The results further demonstrate the efficiency of our approach.\&nbsp;},
	author = {Rauniyar, Saurab and Srivastava, Abhishek and Vabesh Kumar {Jha} and Ritika Kumari {Jha} and Jha, Debesh and Rauniyar, Ashish}
}
