Abstract
Colorectal cancer is the third most common cancer-related death after lung cancer and breast cancer worldwide. The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy. Computer-aided diagnosis systems have the potential to be applied for polyp screening and reduce the number of missing polyps. In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture. We validated the performance of presented ensemble models on the CVC-Clinic (GIANA 2018) dataset. The DenseNet169 feature extractor combined with U-Net architecture outperformed the other counterparts and achieved an accuracy of 99.15%, Dice similarity coefficient of 90.87%, and Jaccard index of 83.82%.
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References
CVC-ColonDB. http://mv.cvc.uab.es/projects/colon-qa/cvccolondb
GianaDataset. https://giana.grand-challenge.org/PolypSegmentation/
Key Statistics for Colorectal Cancer. https://www.cancer.org/cancer/colon-rectal-cancer/about/key-statistics.html
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Hosseinzadeh Kassani, S., Hosseinzadeh Kassani, P., Wesolowski, M.J., Schneider, K.A., Deters, R. (2020). Automatic Polyp Segmentation Using Convolutional Neural Networks. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_29
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