Deep Learning-Based Comparative Study to Detect Polyp Removal in Endoscopic Images


Musha, Ahmmad and Hasnat, Rehnuma and Al Mamun, Abdullah Sarwar and Ghosh, Tonmoy (2022) Deep Learning-Based Comparative Study to Detect Polyp Removal in Endoscopic Images. In: 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), 9-11 March 2022, Pune, India.

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Polyps are one of the most common gastrointestinal diseases. It has the potential to cause fatal colon and rectal cancers. As a result, it must be removed during the primitive stage. In this paper, we developed an algorithm that uses endoscopy images to detect polyp removal status. We investigated convolutional neural networks such as DenseNet, ResNet, VGG, MobileNet, and others to extract features from images and then use those features to classify whether a polyp is completely removed or not. 1000 dyed resection margins and 1000 dyed and lifted polyps' images from a publicly available dataset were used to test and train the proposed models. On the testing dataset, we obtained 85% sensitivity, 88% precision, and 85% fl-scores by using MobileNet architecture. This computer-aided polyp removal method assists physicians in diagnosing polyp status in a reliable, quick, and cost-effective manner.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Training, Sensitivity, Endoscopes, Computational modeling, Medical services, Feature extraction, Convolutional neural networks
Subjects: R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions: Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 06 Oct 2022 02:13
Last Modified: 06 Oct 2022 02:13


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