Transfer learning for Endoscopy disease detection and segmentation with mask-RCNN benchmark architecture

Rezvy, Shahadate, Zebin, Tahmina, Braden, Barbara, Pang, Wei, Taylor, Stephen and Gao, Xiahong (2020) Transfer learning for Endoscopy disease detection and segmentation with mask-RCNN benchmark architecture. In: 2020 IEEE 17th International Symposium on Biomedical Imaging, 2020-04-03 - 2020-04-07, Iowa City.

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Abstract

We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset. On the images provided for the phase-I test dataset, for 'BE', we achieved an average precision of 51.14%, for 'HGD' and 'polyp' it is 50%. However, the detection score for 'suspicious' and 'cancer' were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase -II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: deep learning,computer vision,endoscopy, gastrointestinal
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 08 Apr 2020 00:24
Last Modified: 29 Sep 2020 23:58
URI: https://ueaeprints.uea.ac.uk/id/eprint/74726
DOI:

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